Vorhersage der Weinqualität von Vinho Verde

Dieses Jupyter Notebook widmet sich der Vorhersage von Weinqualität des Vinho Verde.

Vinho Verde, oder auch der grüne Wein ist ein junger portugisischer Wein, der aus den Anbaugebieten zwischen den Flüssen Douro und Minho im Norden Portugals stammt.

Als Rebsorten dienen unter anderem Alvarinho, Avesso, Loureiro und Trajadura. Gelegentlich wird auch die Rebsorte Sercial verwendet.

Das Leben ist zu kurz, um schlechten Wein zu trinken.
- Johann Wolfgang von Goethe.

Durchlauf für Professoren und Kommilitonen

Um die Trainingsprozesse zu verkürzen kann man einen Schnelldurchlauf aktivieren. Dieser wurde aus den Ergebnissen des Hyperparametertrainings erstellt und enthält bereits beschränkte Parameterbereiche, die zu einem ausreichend guten Ergebnis führen. Dies verkürzt die Laufzeit von Stunden auf <3 Minuten. Die kompletten Durchläufe befinden sich in den CSV-Datei in dem "results" Ordner.

Die Evaluation/Ergebnisse richten sich nach diesen CSV-Dateien.

# Professor oder Kommiltonen?
test_for_professor = False

Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
import time
from scipy import stats

from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection  import RepeatedKFold, KFold
from sklearn import metrics
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR

import matplotlib.patches as mpatches

# Warnings wurden am Ende rausgefiltert, da "FutureWarnings" durch ältere Funktionen - welche für uns sinnvoller erschienen - 
# erschienen und beim flüssigen Lesen stören.
import warnings
warnings.filterwarnings('ignore')

seed = 42

Hilfsfunktionen

Definierte Hilfsfunktionen (hauptsächlich für Grafiken), um ein flüssigeres Lesen zu ermöglichen.

def plot_correct_prediction_pairplot(y, y_pred, df):
    """Pairplot, um die Lokalität der falschen Vorhersagen darzustellen
    
    Args:
        y (np.array): Labels des Datensatzes
        y_pred (np.array): Vorhersagen des Modells
        df (pandas.DataFrame): DatenFrame des Datensatzes
    """
    plot_df = df.copy(deep=True)
    plot_df["correct_prediction"] = np.equal(y, np.round(y_pred))
    
    plt.rc('axes', labelsize=14) 
    plt.rc('xtick', labelsize=16) 
    plt.rc('ytick', labelsize=16) 
    p = sns.pairplot(plot_df.drop("quality", axis=1), hue="correct_prediction",palette = sns.color_palette("tab10", n_colors=2), plot_kws={'alpha': 0.5})

def plot_metrics_ensemble(y_test, y_pred, predictions, modelname="MODELNAME"):
    """Plot der Metriken Accuracy, Mean Absolut Error, Mean Squared Error 
    
    Args:
        y_test (np.array): Labels des Testdatensatzes
        y_pred (np.array): Vorhersagen des Modells
        modelname (str): Modellname für die Plots, Defaults ist "MODELNAME"
    """
    metric_list = np.asarray([[metrics.accuracy_score(np.round(predictions[0]),y_test), metrics.accuracy_score(np.round(predictions[1]),y_test), metrics.accuracy_score(np.round(y_pred),y_test)],
                    [metrics.mean_absolute_error(predictions[0],y_test), metrics.mean_absolute_error(predictions[1],y_test), metrics.mean_absolute_error(y_pred,y_test)],
                    [metrics.mean_squared_error(predictions[0],y_test), metrics.mean_squared_error(predictions[1],y_test), metrics.mean_squared_error(y_pred,y_test),]
                    ])
    
    plt.figure(figsize=(8,6))

    ax = plt.subplot(1, 1, 1)
    p = sns.heatmap(metric_list, annot=True, linewidths=.5, cbar = False, annot_kws={"fontsize":18})
    p.set_yticklabels(["Accuracy", "Mean Absolute Error", "Mean Squared Error"], size = 16, rotation=0)

    p.set_xticklabels(["SVR", "RFR", "Ensemble"], size = 16)
    ax.set_title("Ergebnisse "+modelname+" (Testdatensatz)",fontsize=20)

    plt.tight_layout()
    plt.show()
    
def plotEverything_2(df):
    """Plot aller Features des DataFrames
    
    Args:
        df (pandas.DataFrame): DatenFrame
    """
    plt.figure(figsize = (12, 20))
    plotnumber = 1

    for col in df:
        if plotnumber <= 12:
            ax = plt.subplot(6, 2, plotnumber)
            p = sns.distplot(df[col])
            plt.xlabel(col, fontsize = 14)
            plt.ylabel('')

        plotnumber += 1

    plt.tight_layout()
    plt.show()

def plot_correlatedfeatures():
    """Plot der Features mit den höchsten Korrelationen zueinander
    """
    fig, axes = plt.subplots(1,3,figsize=(25,8))

    p = sns.regplot(ax=axes[0], x="density", y="alcohol", data=df, line_kws={'color': 'grey'})
    p.tick_params(labelsize=16)
    p.set_xlabel(p.get_xlabel(),fontsize=22)
    p.set_ylabel(p.get_ylabel(),fontsize=22)
    p = sns.regplot(ax=axes[1],x="density", y="residual sugar",data=df, line_kws={'color': 'grey'})
    p.tick_params(labelsize=16)
    p.set_xlabel(p.get_xlabel(),fontsize=22)
    p.set_ylabel(p.get_ylabel(),fontsize=22)
    p = sns.regplot(ax=axes[2],x="chlorides", y="alcohol",data=df, line_kws={'color': 'grey'})
    p.tick_params(labelsize=16)
    p.set_xlabel(p.get_xlabel(),fontsize=22)
    p.set_ylabel(p.get_ylabel(),fontsize=22)
    
    plt.show()

def plot_correlationmatrix(train_df, threshold = 0):
    """Plot der Korrelationsmatrix des Datensatzes mit gegebener Schwelle
    
    Args:
        train_df (pandas.DataFrame): Datensatz
        threshold (float): Schwelle, ab welchem Korrelationswert der Wert mit geplotet werden soll, Defaults ist 0.
    """
    correlation_matrix = train_df.corr()
    r = []
    for w in np.round(correlation_matrix.values.reshape(-1),2):
        if w > threshold or w < -threshold:
            r.append(str(w))
        else:
            r.append("")
    r = np.array(r).reshape(len(train_df.columns),len(train_df.columns))
    f, ax = plt.subplots(figsize=(9, 6))
    sns.heatmap(correlation_matrix, annot=r, linewidths=.1,ax=ax,fmt="s",cmap=sns.diverging_palette(150, 275, s=80, l=55, n=9), vmin=-1, vmax=1)

def plot_metrics(y_test, y_pred, best_config, modelname="MODELNAME"):
    """Plot der Metriken Accuracy, Mean Absolut Error, Mean Squared Error 
    
    Args:
        y_test (np.array): Labels des Testdatensatzes
        y_pred (np.array): Vorhersagen des Modells
        modelname (str): Modellname für die Plots, Defaults ist "MODELNAME"
    """
    metric_list = np.asarray([[best_config["acc_train"], best_config["acc_val"], metrics.accuracy_score(np.round(y_pred),y_test)],
                    [best_config["mse_train"], best_config["mse_val"], metrics.mean_squared_error(y_pred,y_test),]
                    ])
    
    plt.figure(figsize=(8,6))

    ax = plt.subplot(1, 1, 1)
    p = sns.heatmap(metric_list, annot=True, linewidths=.5, cbar = False, annot_kws={"fontsize":18})
    p.set_yticklabels(["Accuracy", "Mean Squared Error"], size = 16, rotation=0)

    p.set_xticklabels(["Train", "Validation", "Test"], size = 16)
    ax.set_title("Ergebnisse "+modelname,fontsize=20)

    plt.tight_layout()
    plt.show()
    
def plot_rfrfeatureimportance(rfr_model, train_df):
    """Plot der wichtigsten Features des Random Forest Regressor Modells
    
    Args:
        rfr_model (sklearn.ensemble.RandomForestRegressor): Random Forest Regressor Modell
        train_df (pandas.DataFrame): Datenframe (benötigt für die Columns)
    """
    index = np.argsort(rfr_model.feature_importances_)
    performance = rfr_model.feature_importances_[index]
    objects = train_df.drop('quality',axis=1).columns[index]
    y_pos = np.arange(len(objects))

    plt.figure(figsize=(12,8))
    plt.barh(y_pos, performance, align='center', alpha=0.5)
    plt.yticks(y_pos, objects, fontsize=14)
    plt.xticks(fontsize=14)
    plt.xlabel('feature_importances', fontsize=16)
    plt.title('Feature importance Random Forest Regressor', fontsize=20)

    plt.show()
    
def plot_rfr2d_2():
    """2D Plots des Random Forest Regressors
    """
    df = pd.read_csv('results/train_conf_tree.csv')  


    df_min = df[df["min_samples_split"] == 2]
    df_min = df_min[df_min["max_features"] =="log2"]
    df_min=df_min.sort_values(by=['max_depth'])
    fig = plt.figure(figsize = (8,5))
    
    ax = fig.add_subplot(111)
    ax.set_title('Veränderung des MSE im Vergleich zur \n max Tief beim Random Forest', fontsize=20)
    ax.plot(df_min["max_depth"], df_min["mse_val"], '-', label='Validation')
    ax.plot(df_min["max_depth"], df_min["mse_train"], '-', label='Train')
    ax2 = ax.twinx()
    ax2.plot(df_min["max_depth"], df_min["time"], '-',label='time(s) for CV', color='black')
    ax2.legend(loc=3)
    ax.grid()
    ax.set_xlabel("max_depth")
    ax.set_ylabel("MSE")
    ax2.set_ylabel("Time (s)")

    df_min=df_min.sort_values(by=['mse_val'])
    ax.plot(df_min["max_depth"][:1], df_min["mse_val"][:1],  '', color = 'red', marker = 'o', label='min mse')

    df_min=df_min.sort_values(by=['mse_train'])
    ax.plot(df_min["max_depth"][:1], df_min["mse_train"][:1],  '',color = 'red', marker = 'o')
    
    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')

    plt.show()

    df_ = df[df["max_features"] =="log2"]
    df_=df_.sort_values(by=['min_samples_split'])
    df_min = df_[df_["max_depth"] == 23]

    fig = plt.figure(figsize = (8,5))
    ax = fig.add_subplot(111)
    ax.plot(df_min["min_samples_split"], df_min["mse_val"], '-', label='Validation')
    ax.plot(df_min["min_samples_split"], df_min["mse_train"], '-', label='Train')
    ax.set_title('Veränderung des MSE im Vergleich zur \n min_samples_split  beim Random Forest', fontsize=20)

    ax2 = ax.twinx()
    ax2.plot(df_min["min_samples_split"], df_min["time"], '-', label="time(s) for CV", color='black')
    ax2.legend(loc=3)
    ax.grid()
    ax.set_xlabel("min_samples_split")
    ax.set_ylabel("MSE")
    ax.invert_xaxis()
    ax2.set_ylabel("Time (s)")

    df_min=df_min.sort_values(by=['mse_val'])
    ax.plot(df_min["min_samples_split"][:1], df_min["mse_val"][:1],  '' ,marker = 'o', color = 'red', label='min mse')

    df_min=df_min.sort_values(by=['mse_train'])
    ax.plot(df_min["min_samples_split"][:1], df_min["mse_train"][:1],  '' ,marker = 'o',color = 'red')

    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')
    plt.show()
    
def plot_histograms(y_test, y_pred, modelname="MODELNAME"):
    """Histogrammplots der wahren Values, der gerundeten Vorhersagen und der wahren Vorhersagen
    
    Args:
        y_test (np.array): Labels des Testdatensatzes
        y_pred (np.array): Vorhersagen des Modells
        modelname (str): Modellname für die Plots, Defaults ist "MODELNAME"
    """
    plt.figure(figsize = (16, 5))

    ax = plt.subplot(1, 3, 1)
    p = sns.distplot(y_test)
    p.tick_params(labelsize=12)
    ax.set_ylabel("Number of Datapoints",fontsize=18)
    ax.set_title("Ground Truth",fontsize=20)

    ax = plt.subplot(1, 3, 2)
    p = sns.distplot(np.round(y_pred))
    p.tick_params(labelsize=12)
    ax.set_xlabel("Quality",fontsize=18)
    ax.set_ylabel("Number of Datapoints",fontsize=0)
    ax.set_title("Rounded Prediction "+modelname,fontsize=20)

    ax = plt.subplot(1, 3, 3)
    p = sns.distplot(y_pred)
    p.tick_params(labelsize=12)
    ax.set_ylabel("Number of Datapoints",fontsize=0)
    ax.set_title("Prediction "+modelname,fontsize=20)

    plt.tight_layout()
    plt.show()
    
def plot_confusion_matrix(y_test, y_pred, modelname="MODELNAME", datensatz="DATENSATZ"):
    """Plot der Confusion Matrix mit Recall und Precision
    
    Args:
        y_test (np.array): Labels des Testdatensatzes
        y_pred (np.array): Vorhersagen des Modells
        modelname (str): Modellname für die Plots, Defaults ist "MODELNAME"
        datensatz (str): Datensatz für die Plots, Defaults ist "DATENSATZ"
    """
    mat = metrics.confusion_matrix(y_test, np.round(y_pred))
    cr = metrics.classification_report(y_test, np.round(y_pred),output_dict=True)

    cr_list = []
    for i in np.arange(3,10,1):
        tmp_list = []
        tmp_list.append(cr[str(i)]["precision"])    
        tmp_list.append(cr[str(i)]["recall"])
        cr_list.append(tmp_list)

    cr_list = np.asarray(cr_list)

    plt.figure(figsize = (16, 7))

    ax = plt.subplot(1, 2, 1)
    p = sns.heatmap(mat, annot=True,fmt="d", linewidths=.5, cbar = False, annot_kws={"fontsize":14})
    p.set_yticklabels(np.sort(np.unique(y_test)), size = 16)
    p.set_xticklabels(np.sort(np.unique(y_test)), size = 16)
    ax.set_xlabel("Rounded Prediction of Quality",fontsize=18)
    ax.set_ylabel("Ground Truth Quality",fontsize=18)
    ax.set_title("Confusion Matrix "+modelname+" "+datensatz,fontsize=20)

    palette = sns.color_palette("coolwarm", as_cmap=False)
    palette.reverse()
    ax = plt.subplot(1, 2, 2)
    p = sns.heatmap(cr_list, annot=True, cbar = False, linewidths=.5, annot_kws={"fontsize":14})
    p.set_xticklabels(["Precision", "Recall"], size = 16)
    p.set_yticklabels(np.sort(np.unique(y_test)), size = 0)
    ax.set_title("Precision/Recall "+modelname+" "+datensatz,fontsize=20)

    plt.tight_layout()
    plt.show()

def plotEverything(df):
    """Plot aller Features des DataFrames
    
    Args:
        df (pandas.DataFrame): DatenFrame
    """
    plt.figure(figsize = (25, 20))
    plotnumber = 1

    for col in df:
        if plotnumber <= 12:
            ax = plt.subplot(4, 3, plotnumber)
            sns.distplot(df[col])
            plt.xlabel(col, fontsize = 15)
            plt.ylabel('')

        plotnumber += 1

    plt.tight_layout()
    plt.show()

class CrossValidationValueRecorder:
    """Klasse um die Metriken der Crossvalidation zwischenzuspeichern
    """
    
    def __init__(self):
        """Initialisierung der Klasse
        """
        self.mse_val=[]
        self.acc_val=[]
        self.r2score_val=[]

        self.mse_train=[]
        self.acc_train=[]
        self.r2score_train=[]
        
    def Reset(self):
        """Reset der Klasse
        """
        self.mse_val=[]
        self.acc_val=[]
        self.r2score_val=[]

        self.mse_train=[]
        self.acc_train=[]
        self.r2score_train=[]
    
    def UpdateConfig(self, train_config):
        """Updaten der Trainingsconfig
        
        Args:
            train_config (dict): Trainingsconfiguration
        """
        train_config["mse_val"].append(np.mean(self.mse_val))
        train_config["mse_train"].append(np.mean(self.mse_train))

        train_config["acc_val"].append(np.mean(self.acc_val))
        train_config["acc_train"].append(np.mean(self.acc_train))

        train_config["r2score_val"].append(np.mean(self.r2score_val))
        train_config["r2score_train"].append(np.mean(self.r2score_train))
            
    def AddValidationMetric(self, y_val, y_pred):
        """Hinzufügen der Validationsmetriken
        
        Args:
            y_val (np.array): Labels des Validierungsdatensatzes
            y_pred (np.array): Vorhersagen des Modells
        """
        self.mse_val.append(metrics.mean_squared_error(y_val, y_pred))
        self.acc_val.append(metrics.accuracy_score(np.rint(y_val).astype(int),np.rint(y_pred).astype(int)))
        self.r2score_val.append(metrics.r2_score(y_val,y_pred))
    
    def AddTrainMetric(self, y_train, y_pred):
        """Hinzufügen der Trainingsmetriken
        
        Args:
            y_train (np.array): Labels des Trainingsdatensatzes
            y_pred (np.array): Vorhersagen des Modells
        """
        self.mse_train.append(metrics.mean_squared_error(y_train, y_pred))
        self.acc_train.append(metrics.accuracy_score(np.rint(y_train).astype(int),np.rint(y_pred).astype(int)))
        self.r2score_train.append(metrics.r2_score(y_train,y_pred))
    
    def GetMSE(self):
        """Hinzufügen der Trainingsmetriken
        
        Returns:
            np.mean(self.mse_val) (np.array): Durchschnitt des Mean Squared Errors auf den Validierungsdatensatz
        """
        return np.mean(self.mse_val)
    
    
def ProductLength(hyperparameters):
    """Berechnung der Durchläufe eines Trainings
    
    Args:
        hyperparameters (list): Liste aller veränderlichen Hyperparameter

    Returns:
        length (int): Berechnete Anzahl Durchläufe eines Trainings
    """
    length = 1
    for p in hyperparameters:
        sl = max(len(p),1)
        length = length * sl
    return length

def plot_logchanges(df):
    """Plot um die Veränderungen der Features durch das Logarithmieren aufzuzeigen
    
    Args:
        df (pandas.DataFrame): Datensatz
    """
    plt.figure(figsize = (16, 10))
    plotnumber = 1

    for col in ["alcohol", "alcohol_Logarithm", "residual sugar", "residual sugar_Logarithm"]:
        ax = plt.subplot(2, 2, plotnumber)
        sns.distplot(df[col])
        plt.xlabel(col, fontsize = 15)
        plt.ylabel('')
        plotnumber += 1

    plt.show()

def plot_rfr3d():
    """3D Plots des Random Forest Regressors 
    """
    df = pd.read_csv("results/train_conf_tree.csv")
    df_sqrt = df.loc[df['max_features'] == "sqrt"]
    df_log2 = df.loc[df['max_features'] == "auto"]

    fig = plt.figure(figsize=(16,10))

    ##first plot
    ax = fig.add_subplot(1, 2, 1, projection='3d')
    ax.set_title("MSE: sqrt")
    ax.set_xlabel('max_depth')
    ax.set_ylabel('min_samples_split')
    ax.set_zlabel('mse')

    plot_val = ax.plot_trisurf(df_sqrt["max_depth"].values, df_sqrt["min_samples_split"].values, df_sqrt["mse_val"],cmap='viridis',label='Validation')
    plot_val._facecolors2d = plot_val._facecolor3d
    plot_val._edgecolors2d = plot_val._edgecolor3d

    plot_train = ax.plot_trisurf(df_sqrt["max_depth"].values, df_sqrt["min_samples_split"].values, df_sqrt["mse_train"],cmap='inferno',label="Train")
    plot_train._facecolors2d = plot_train._facecolor3d
    plot_train._edgecolors2d = plot_train._edgecolor3d

    fig.colorbar(plot_train,location="bottom",shrink=0.7)

    blue_patch = mpatches.Patch(color='tab:blue', label='Validation')
    red_patch = mpatches.Patch(color='firebrick', label='Train')
    ax.legend(handles=[blue_patch,red_patch])

    # second plot
    ax = fig.add_subplot(1, 2, 2, projection='3d')
    ax.set_title("MSE: sqrt")
    ax.set_xlabel('max_depth')
    ax.set_ylabel('min_samples_split')
    ax.set_zlabel('mse_vals')

    plot = ax.plot_trisurf(df_sqrt["max_depth"].values, df_sqrt["min_samples_split"].values, df_sqrt["mse_val"],cmap='viridis', label="Validation")
    plot._facecolors2d = plot._facecolor3d
    plot._edgecolors2d = plot._edgecolor3d
    plt.legend()

    fig.colorbar(plot,location="bottom",shrink=0.7)

    min_x = df_sqrt["max_depth"].iloc[np.argmin(df_sqrt["mse_val"])]
    min_y = df_sqrt["min_samples_split"].iloc[np.argmin(df_sqrt["mse_val"])]
    min_z = df_sqrt["mse_val"].iloc[np.argmin(df_sqrt["mse_val"])]

    ax.plot(min_x,min_y,min_z,marker="o",c=plt.cm.coolwarm(1.))

def plot_svr_3d():
    """3D Plots des Support Vector Regressors
    """
    df = pd.read_csv("results/train_conf_svr.csv")

    df_std = df.loc[df['scaler'] == "StandardScaler()"]
    df_minmax = df.loc[df['scaler'] == "MinMaxScaler()"]

    df_std = df_std.loc[df_std['gamma'] == "0.31622776601683794"]
    df_minmax = df_minmax.loc[df_minmax['gamma'] == "0.31622776601683794"]

    fig = plt.figure(figsize=(16,10))
    # first plot
    
    ax = fig.add_subplot(1, 2, 1, projection='3d')
    ax.set_title("MSE: StandardScaler")
    ax.set_xlabel('C')
    ax.set_ylabel('epsilon')
    ax.set_zlabel('mse')

    plot_val = ax.plot_trisurf(df_std["C"].values, df_std["epsilon"].values, df_std["mse_val"],cmap='viridis',label='Validation')
    plot_val._facecolors2d = plot_val._facecolor3d
    plot_val._edgecolors2d = plot_val._edgecolor3d

    plot_train = ax.plot_trisurf(df_std["C"].values, df_std["epsilon"].values, df_std["mse_train"],cmap='inferno',label="Train")
    plot_train._facecolors2d = plot_train._facecolor3d
    plot_train._edgecolors2d = plot_train._edgecolor3d
    
    fig.colorbar(plot_train,location="bottom",shrink=0.7)

    blue_patch = mpatches.Patch(color='tab:blue', label='Validation')
    red_patch = mpatches.Patch(color='firebrick', label='Train')
    ax.legend(handles=[blue_patch,red_patch])
    
    
    # second plot
   
    ax = fig.add_subplot(1, 2, 2, projection='3d')
    ax.set_title("MSE: StandardScaler")
    ax.set_xlabel('C')
    ax.set_ylabel('epsilon')
    ax.set_zlabel('mse_vals')

    plot = ax.plot_trisurf(df_std["C"].values, df_std["epsilon"].values, df_std["mse_val"].values,cmap='viridis', label="Validation")
    plot._facecolors2d = plot._facecolor3d
    plot._edgecolors2d = plot._edgecolor3d
    plt.legend()

    fig.colorbar(plot,location="bottom",shrink=0.7)

    min_x = df_std["C"].iloc[np.argmin(df_std["mse_val"])]
    min_y = df_std["epsilon"].iloc[np.argmin(df_std["mse_val"])]
    min_z = df_std["mse_val"].iloc[np.argmin(df_std["mse_val"])]
    ax.plot(min_x,min_y,min_z,marker="o",c=plt.cm.coolwarm(1.))
    
def plot_svr_feat_sel3d():
    """3D Plots des Support Vector Regressors mit Feature Selection
    """
    df = pd.read_csv("results/train_conf_svr_feat_sel.csv")

    df_minmax = df.loc[df['scaler'] == "MinMaxScaler()"]
    df_minmax = df_minmax.loc[df_minmax['gamma'] == "scale"]

    fig = plt.figure(figsize = (16,10))

    ax = fig.add_subplot(1, 2, 1, projection='3d')
    ax.set_title("MSE: MinMaxScaler")
    ax.set_xlabel('C')
    ax.set_ylabel('epsilon')
    ax.set_zlabel('mse')

    plot_val = ax.plot_trisurf(df_minmax["C"].values, df_minmax["epsilon"].values, df_minmax["mse_val"],cmap='viridis',label='Validation')
    plot_val._facecolors2d = plot_val._facecolor3d
    plot_val._edgecolors2d = plot_val._edgecolor3d

    plot_train = ax.plot_trisurf(df_minmax["C"].values, df_minmax["epsilon"].values, df_minmax["mse_train"],cmap='inferno',label="Train")
    plot_train._facecolors2d = plot_train._facecolor3d
    plot_train._edgecolors2d = plot_train._edgecolor3d

    fig.colorbar(plot_train,location="bottom",shrink=0.7)

    blue_patch = mpatches.Patch(color='tab:blue', label='Validation')
    red_patch = mpatches.Patch(color='firebrick', label='Train')
    ax.legend(handles=[blue_patch,red_patch])


    ax = fig.add_subplot(1, 2, 2, projection='3d')
    ax.set_title("MSE: MinMaxScaler")
    ax.set_xlabel('C')
    ax.set_ylabel('epsilon')
    ax.set_zlabel('mse_vals')

    plot = ax.plot_trisurf(df_minmax["C"].values, df_minmax["epsilon"].values, df_minmax["mse_val"].values,cmap='viridis', label="Validation")
    plot._facecolors2d = plot._facecolor3d
    plot._edgecolors2d = plot._edgecolor3d
    plt.legend()

    fig.colorbar(plot,location="bottom",shrink=0.7)

    min_x = df_minmax["C"].iloc[np.argmin(df_minmax["mse_val"])]
    min_y = df_minmax["epsilon"].iloc[np.argmin(df_minmax["mse_val"])]
    min_z = df_minmax["mse_val"].iloc[np.argmin(df_minmax["mse_val"])]

    ax.plot(min_x,min_y,min_z,marker="o",c=plt.cm.coolwarm(1.))
    
    
def plot_rfr2d():
    """2D Plots des Random Forest Regressors
    """
    df = pd.read_csv('results/train_conf_tree.csv')  


    df_min = df[df["min_samples_split"] == 2]
    df_min = df_min[df_min["max_features"] =="log2"]
    df_min=df_min.sort_values(by=['max_depth'])
    fig = plt.figure(figsize = (8,5))
    
    ax = fig.add_subplot(111)
    ax.set_title('Veränderung des MSE im Vergleich zur \n max Tief beim Random Forest', fontsize=20)
    ax.plot(df_min["max_depth"], df_min["mse_val"], '-', label='Validation')
    ax.plot(df_min["max_depth"], df_min["mse_train"], '-', label='Train')
    ax2 = ax.twinx()
    ax2.plot(df_min["max_depth"], df_min["time"], '-',label='time(s) for CV', color='black')
    ax2.legend(loc=3)
    ax.grid()
    ax.set_xlabel("max_depth")
    ax.set_ylabel("MSE")
    ax2.set_ylabel("Time (s)")

    df_min=df_min.sort_values(by=['mse_val'])
    ax.plot(df_min["max_depth"][:1], df_min["mse_val"][:1],  '', color = 'red', marker = 'o', label='min mse')

    df_min=df_min.sort_values(by=['mse_train'])
    ax.plot(df_min["max_depth"][:1], df_min["mse_train"][:1],  '',color = 'red', marker = 'o')
    
    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')
    
    ax.text(1.5, 0.5, 'Bei dem Plot wird die  min_samples_split  auf zwei gesetzt und den max_features auf log2,\nda so die besten Ergebnis erreicht werden. Der Plot zeigt die Veränderung des MSE in Abhängigkeit \nder  maximale tief der Bäume. Es wird erkennbar, dass kaum eine Verbesserung festgestellt werden \nkann,  bei einer tiefe > 20.  Anhand der Zeit wird erkennbar das die Model bei einer maximale \nTief nicht mehr komplexer werde. Da die Bäume bereits die maximal aufgespannt sind. ',
    verticalalignment='bottom', horizontalalignment='left',
    transform=ax.transAxes,
    color='black', fontsize=15)

    plt.show()

    df_ = df[df["max_features"] =="log2"]
    df_=df_.sort_values(by=['min_samples_split'])
    df_min = df_[df_["max_depth"] == 23]

    fig = plt.figure(figsize = (8,5))
    ax = fig.add_subplot(111)
    ax.plot(df_min["min_samples_split"], df_min["mse_val"], '-', label='Validation')
    ax.plot(df_min["min_samples_split"], df_min["mse_train"], '-', label='Train')
    ax.set_title('Veränderung des MSE im Vergleich zur \n min_samples_split  beim Random Forest', fontsize=20)

    ax.text(1.5, 0.5, 'Bei dem Plot wird die  max_depth  auf 23 gesetzt und den max_features auf log2,\nda so die besten Ergebnis erreicht werden. Der Plot zeigt die Veränderung des MSE in Abhängigkeit \nder  min_samples_split. Es wird erkennbar, das ein maximale Aufteilung der Daten am sinnvollsten ist, \nalso min_samples_split =2',
    verticalalignment='bottom', horizontalalignment='left',
    transform=ax.transAxes,
    color='black', fontsize=15)

    ax2 = ax.twinx()
    ax2.plot(df_min["min_samples_split"], df_min["time"], '-', label="time(s) for CV", color='black')
    ax2.legend(loc=3)
    ax.grid()
    ax.set_xlabel("min_samples_split")
    ax.set_ylabel("MSE")
    ax.invert_xaxis()
    ax2.set_ylabel("Time (s)")

    df_min=df_min.sort_values(by=['mse_val'])
    ax.plot(df_min["min_samples_split"][:1], df_min["mse_val"][:1],  '' ,marker = 'o', color = 'red', label='min mse')

    df_min=df_min.sort_values(by=['mse_train'])
    ax.plot(df_min["min_samples_split"][:1], df_min["mse_train"][:1],  '' ,marker = 'o',color = 'red')

    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')
    plt.show()

def plot_svr2d():
    """2D Plots des Support Vector Regressors
    """
    df = pd.read_csv('results/train_conf_svr.csv')  
    df_svr=df.sort_values(by=['gamma'])

    df_svr = df_svr[df_svr["gamma"] == '0.31622776601683794']
    df_svr = df_svr[df_svr["scaler"] == 'StandardScaler()']
    df_svr = df_svr[df_svr["C"] < 1.43]
    df_svr = df_svr[df_svr["C"] > 1.42]
    df_svr=df_svr.sort_values(by=['epsilon'])

    fig = plt.figure(figsize = (8,5))
    ax = fig.add_subplot(111)
    ax.plot(df_svr["epsilon"], df_svr["mse_val"], '-', label=' Validation StandardScaler')
    ax.plot(df_svr["epsilon"], df_svr["mse_train"], '-', label=' Train StandardScaler')
    
    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red')
    
    ax2 = ax.twinx()
    ax2.plot(df_svr["epsilon"], df_svr["time"], '-', label="StandardScaler: time(s) for CV", color='black')
    
    
    df_svr=df.sort_values(by=['gamma'])

    df_svr = df_svr[df_svr["gamma"] =='scale']
    df_svr = df_svr[df_svr["scaler"] == 'MinMaxScaler()']
    df_svr = df_svr[df_svr["C"] < 1.43]
    df_svr = df_svr[df_svr["C"] > 1.42]
    df_svr=df_svr.sort_values(by=['epsilon'])

    ax.plot(df_svr["epsilon"], df_svr["mse_val"], '-', label=' Validation MinMaxScaler')
    ax.plot(df_svr["epsilon"], df_svr["mse_train"], '-', label=' Train MinMaxScaler')

    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red', label='min mse')


    ax.set_title('Veränderung des MSE im Vergleich zu \n Epsilon  beim SVR', fontsize=20)


    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')
    ax2.legend(bbox_to_anchor=(1.1, 0),loc='lower left')
    ax.grid()
    ax.set_xlabel("epsilon")
    ax.set_ylabel("MSE")
    ax2.set_ylabel("Time (s)")
    plt.show()
    
    df_svr = df[df["gamma"] =='0.31622776601683794']
    df_svr = df_svr[df_svr["scaler"] == 'StandardScaler()']
    df_svr = df_svr[df_svr["epsilon"] == 0.25]
    df_svr=df_svr.sort_values(by=['C'])

    fig = plt.figure(figsize = (8,5))
    ax = fig.add_subplot(111)
    ax.plot(df_svr["C"], df_svr["mse_val"], '-', label=' Validation StandardScaler')
    ax.plot(df_svr["C"], df_svr["mse_train"], '-', label=' Train StandardScaler')

    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["C"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["C"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red')
    
    ax2 = ax.twinx()
    
    ax2.plot(df_svr["C"], df_svr["time"], '-', label="StandardScaler: time(s) for CV", color='black')
        
    df_svr = df[df["gamma"] =='scale']
    df_svr = df_svr[df_svr["scaler"] == 'MinMaxScaler()']
    df_svr = df_svr[df_svr["epsilon"] == 0.25]
    df_svr=df_svr.sort_values(by=['C'])
    ax.plot(df_svr["C"], df_svr["mse_val"], '-', label=' Validation MinMaxScaler')
    ax.plot(df_svr["C"], df_svr["mse_train"], '-', label=' Train MinMaxScaler')

    

    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["C"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["C"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red',label='min mse')

    ax.set_title('Veränderung des MSE im Vergleich zu \n C  beim SVR', fontsize=20)

    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
    ax2.legend(bbox_to_anchor=(1.1, 0),loc='lower left')
    ax.grid()
    ax.set_xlabel("C")
    ax.set_ylabel("MSE")
    ax2.set_ylabel("Time (s)")
    plt.show()

def plot_svr2d_feat_sel():
    """2D Plots des Support Vector Regressors mit Feature Selection
    """
    df = pd.read_csv('results/train_conf_svr_feat_sel.csv')  
    df=df.sort_values(by=['mse_val'])
    best_config=df.iloc[0].to_dict()

    df_svr = df.sort_values(by=['gamma'])

    df_svr = df_svr[df_svr["gamma"] =="scale"]
    df_svr = df_svr[df_svr["scaler"] == 'StandardScaler()']
    df_svr = df_svr[df_svr["C"] < 1.43]
    df_svr = df_svr[df_svr["C"] > 1.42]
    df_svr=df_svr.sort_values(by=['epsilon'])

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(df_svr["epsilon"], df_svr["mse_val"], '-', label=' Validation StandardScaler')
    ax.plot(df_svr["epsilon"], df_svr["mse_train"], '-', label=' Train StandardScaler')
    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red')

    df_svr=df.sort_values(by=['gamma'])

    df_svr = df_svr[df_svr["gamma"] =="scale"]
    df_svr = df_svr[df_svr["scaler"] == 'MinMaxScaler()']
    df_svr = df_svr[df_svr["C"] < 1.43]
    df_svr = df_svr[df_svr["C"] > 1.42]
    df_svr=df_svr.sort_values(by=['epsilon'])

    ax.plot(df_svr["epsilon"], df_svr["mse_val"], '-', label=' Validation MinMaxScaler')
    ax.plot(df_svr["epsilon"], df_svr["mse_train"], '-', label=' Train MinMaxScaler')

    ax.set_title('Veränderung des MSE im Vergleich zu \n Epsilon  beim SVR mit Feature Selection', fontsize=20)

    ax2 = ax.twinx()
    ax2.plot(df_svr["epsilon"], df_svr["time"], '-', label="StandardScaler: time(s) for CV", color='black')
    
    df_svr=df_svr.sort_values(by=['mse_val'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_val"][:1],  '' ,marker = 'o', color = 'red')

    df_svr=df_svr.sort_values(by=['mse_train'])
    ax.plot(df_svr["epsilon"][:1], df_svr["mse_train"][:1],  '' ,marker = 'o',color = 'red', label='min mse')
    ax.legend(bbox_to_anchor=(1.1, 1), loc='upper left')
    ax2.legend(bbox_to_anchor=(1.1, 0),loc='lower left')

    ax.grid()
    ax.set_xlabel("epsilon")
    ax.set_ylabel("MSE")
    ax2.set_ylabel("Time (s)")
    plt.show()

Datensatz einlesen

csv = 'data/winequality-white.csv'
df = pd.read_csv(csv,sep=";")

Datenanalyse

Der Datensatz stammt aus dem Machine Learning Repository des UCI und wurde dort am 07.10.2009 eingetragen. Er enthält jeweils Daten für Weiswein und Rotwein des Vinho Verde, wobei für dieses Jupyter Notebook lediglich der Datensatz der Weisweine relevant ist.

Der Datensatz enthält 12 verschiedene Attribute, dabei wurden für diese Arbeit die ersten 11 Attribute zu Featuren deklariert und das letzte Attribut - Quality - zur Zielvariable. Für den Weiswein Datensatz gibt es 4898 Datenpunkte.

Anzumerken ist, dass die Qualität von Weinexperten evaluiert wurde. Dies bedeutet, dass die Weinqualität anhand subjektiven Wahrnehmungen der Experten bestimmt wurde.

Quelle:
[Cortez et al., 2009]

Attribut	Erläuterung	Einheit
Fixed Acidity	Weinsäuren, welche in festem und nichtflüchtigen Zuständen vorkommen	g/dm3
Volatile Acidity	Essigsäure, welche in hohen Konzentrationen zu einem Essiggeschmack führen	g/dm3
Citric Acid	Zitronensäure, welche Weinen in geringer Quantität frische verleihen	g/dm3
Residual Sugar	Zucker, welcher nach der Gärung verbleibt.	g/dm3
Chlorides	Salzgehalt des Weins	g/dm3
Free Sulfur Dioxide	Schwefeldioxid welches als gelöstes Gas und Bisulfit Ion vorkommen. Es verhindert mikrobielles Wachstum und die Oxidation des Weins	g/dm3
Total Sulfur Dioxide	Gesamtmenge des Schwefeldioxid in freier und gebundener Form.	g/dm3
Density	Dichte	g/cm3
pH	Gibt die Säure bzw. Base der Lösung an. 1 = sehr sauer bis 14 = sehr basisch	Diskreter Wert zwischen 1-14
Sulphates	Sulphate, die als Zusatz zum Wein gegeben werden und zum Gehalt der Schwefeloxiden beiträgt , welche antimikrobiell und antioxidativ wirken	g/dm3
Alcohol	Alkoholgehalt des Weins	Volumenprozent
Quality	Median der Weinqualität aus 3 Evaluationen gegeben von Weinexperten 1 = Schlecht 10 = Sehr Gut	Diskreter Wert zwischen 1-10

df

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.0	0.27	0.36	20.7	0.045	45.0	170.0	1.00100	3.00	0.45	8.8	6
1	6.3	0.30	0.34	1.6	0.049	14.0	132.0	0.99400	3.30	0.49	9.5	6
2	8.1	0.28	0.40	6.9	0.050	30.0	97.0	0.99510	3.26	0.44	10.1	6
3	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.99560	3.19	0.40	9.9	6
4	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.99560	3.19	0.40	9.9	6
...	...	...	...	...	...	...	...	...	...	...	...	...
4893	6.2	0.21	0.29	1.6	0.039	24.0	92.0	0.99114	3.27	0.50	11.2	6
4894	6.6	0.32	0.36	8.0	0.047	57.0	168.0	0.99490	3.15	0.46	9.6	5
4895	6.5	0.24	0.19	1.2	0.041	30.0	111.0	0.99254	2.99	0.46	9.4	6
4896	5.5	0.29	0.30	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7
4897	6.0	0.21	0.38	0.8	0.020	22.0	98.0	0.98941	3.26	0.32	11.8	6

4898 rows × 12 columns

Daten Vorverarbeitung

In der Betrachtung der Features im Pairplot sind keine generellen Aufteilungen zu erkennen. Im Vergleich zur Zielvariable - der Weinqualität("quality") - sieht man bei den meisten Features keine großartigen Verschiebungen. Beim Feature Alkohol("alcohol") sieht man eine leichte Verschiebung der Normalverteilungen pro Weinqualität. Es scheint so, als würde ein größerer Alkoholwert eine bessere Weinqualität ausmachen.

Desweiteren sind starke Ausreißer zu erkennen.

sns.pairplot(df, hue="quality", palette = sns.color_palette("tab10", n_colors=7), plot_kws={'alpha': 1})

<seaborn.axisgrid.PairGrid at 0x272db9d7eb0>

Die folgenden Grafiken verdeutlichen, dass die starken Ausreißer teilweise einen vielfach größeren Wert besitzen als die meisten ihres Features. Gerade bei dem Salzgehalt("chlorides"), dem Zucker nach der Gärung("residual sugar") und dem Schwefeldioxid("free sulfur dioxide") ist dies sehr gut zu erkennen. Da diese Werte höchst unwahrscheinlich sind und höchstwahrscheinlich Mess- oder Produktionsfehler darstellen, sollten diese vom Datensatz entfernt werden, da einige Algorithmen stark durch Ausreißer beeinflusst werden.

Ebenso ist anzumerken, dass die Zielvariable höchst unausgeglichen verteilt ist. Es ist deutlich zu erkennen, dass die Klasse $6$ am häufigsten vorkommt und fast gleich viel Datenpunkte beinhaltet wie die zweit und dritthäufigste Klassen $5$ und $7$ zusammen. Dazu sind die Klassen $\leq 4$ und $\geq 8$ fast gar nicht vertreten.

plotEverything_2(df)

Erkennung und Entfernung von Ausreißer

Wie in der oberen Grafik zu sehen ist, befinden sich im Datensatz einige Ausreißer. Zum Detektieren und Entfernen von Ausreißern werden meist die Standard Deviation Methode und die IQR-Methode verwendet.

Quelle:

• https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba

IQR-Methode

Bei der Interquartile Range Methode, kurz $\mathit{IQR}$, wird zunächst der Wert des $\mathit{IQR}$ bestimmt, welcher durch die Differenz des 3. und 1. Quartils berechnet wird. Als Ausreißer werden in der Regel Datenpunkte mit Werten von $1.5*\mathit{IQR}$ oberhalb des oberen Quartils oder unterhalb des unteren Quartils bezeichnet.

Quellen:

• https://de.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/box-whisker-plots/a/identifying-outliers-iqr-rule
• https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba

def remOutliers_IQR(x):
    """Entfernung von Ausreißern im Datensatz mit der IQR-Methode 
    
    Args:
        x (pandas.DataFrame): DatenFrame ohne Zielvariable
        
    Returns:
        x (pandas.DataFrame): DatenFrame ohne Ausreißer
    """
    print("Entfernung von Ausreißern mit der IQR Methode\nShape des Datenframes vor der Anwendung",x.shape)
    
    Q1 = x.quantile(0.25)
    Q3 = x.quantile(0.75)
    IQR = Q3 - Q1
    x = x[~((x < (Q1 - 1.5 * IQR)) |(x > (Q3 + 1.5 * IQR))).any(axis=1)]
    
    print("Shape des Datenframes nach der Anwendung",x.shape)
    
    return x

iqr_df = remOutliers_IQR(df.drop("quality",axis=1))
iqr_df = pd.merge(iqr_df, df["quality"], left_index=True, right_index=True)
iqr_df.reset_index(drop=True, inplace=True)

Entfernung von Ausreißern mit der IQR Methode
Shape des Datenframes vor der Anwendung (4898, 11)
Shape des Datenframes nach der Anwendung (4015, 11)

Mit der $\mathit{IQR}$-Methode werden $883$ Datenpunkte als Ausreißer definiert.

sns.pairplot(iqr_df, hue="quality", palette = sns.color_palette("tab10", n_colors=7), plot_kws={'alpha': 1})

<seaborn.axisgrid.PairGrid at 0x272eb91a460>

plotEverything_2(iqr_df)

Standard Deviation Methode

Bei der Standard Deviation Methode werden mit Hilfe der Standardabweichung und dem Mean Ausreißer identifiziert. Dabei wird der Z-Score wie folgt berechnet:

$$z =\frac{x_i-\mu}{\sigma}$$

In einer Normalverteilung ist davon auszugehen, dass $\mathit{3*\sigma}\;\;$ 99.73% aller Werte enthalten sind. Werte darüber werden in der Regel als Ausreißer angesehen.

Quellen:

• https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/

def remOutliers_ZScore(x):
    """Entfernung von Ausreißern im Datensatz mit der Standard Deviation Methode
    
    Args:
        x (pandas.DataFrame): DatenFrame ohne Zielvariable
        
    Returns:
        x (pandas.DataFrame): DatenFrame ohne Ausreißer
    """
    print("Entfernung von Ausreißern mit der Z-Score Methode\nShape des Datenframes vor der Anwendung",x.shape)
    
    z = np.abs(stats.zscore(x))
    x = x[(z < 3).all(axis=1)]
    
    print("Shape des Datenframes nach der Anwendung",x.shape)
    
    return x

zscore_df = remOutliers_ZScore(df.drop("quality",axis=1))
zscore_df = pd.merge(zscore_df, df["quality"], left_index=True, right_index=True)
zscore_df.reset_index(drop=True, inplace=True)

Entfernung von Ausreißern mit der Z-Score Methode
Shape des Datenframes vor der Anwendung (4898, 11)
Shape des Datenframes nach der Anwendung (4502, 11)

Mit der Standard Deviation Methode werden $396$ Datenpunkte als Ausreißer definiert.

plotEverything_2(zscore_df)

sns.pairplot(zscore_df, hue="quality", palette = sns.color_palette("tab10", n_colors=7), plot_kws={'alpha': 1})

<seaborn.axisgrid.PairGrid at 0x272f7758250>

Wahl der Ausreißer-Methode

Sowohl bei der Standard Deviation Methode, als auch bei der $\mathit{IQR}$-Methode wurden die starken Ausreißer im Datensatz erkannt und eliminiert. Die $\mathit{IQR}$-Methode hat 883 Datenpunkte eliminiert und den Datensatz stärker beschnitten als die Standard Deviation Methode mit 396 Datenpunkten. Aus den Grafiken ist zu erkennen, dass beide Methoden die Ausreißer effektiv beseitigen und beide Methoden eine legitime Wahl für die Entfernung der Ausreißer darstellen. Für den weiteren Verlauf wurde sich für die Standard Deviation Methode entschieden, da dieser alle Ausreißer erkennt und weniger Datenpunkte als die $\mathit{IQR}$-Methode entfernt.

df = zscore_df

Logarithmierung der Features

Für einige Machine Learning Algorithmen ist es vorteilhaft, wenn Features einer Normalverteilung folgen. Dies führt dazu, dass die Kostenfunktion den Fehler der Vorhersagen besser minimiert.

Wie in der Grafik zu sehen, sind einige Features, wie zum Beispiel das Feature "residual sugar", nicht normalverteilt und stark in eine Richtung verzerrt. Durch das Logarithmieren dieser Features lassen sich die Datenpunkte mehr zu einer Normalverteilung transformieren. Diese Transformation werden für die Features "residual sugar" und "alcohol" angewendet.

Quellen:

• https://www.dummies.com/programming/big-data/data-science/transforming-distributions-machine-learning/
• https://medium.com/@ODSC/transforming-skewed-data-for-machine-learning-90e6cc364b0

plotEverything_2(df)

for name in ["alcohol", "residual sugar"]:
    df[name+'_Logarithm'] = df[name].apply(lambda x : np.log(x))

Nach der Logarithmierung gleichen die Features eher einer Normalverteilung.

plot_logchanges(df)

Aufteilung in Trainings- und Testdaten

Die Aufteilung der Daten in Trainings- und Testdatensätze wird mit dem Train_test_split von Scikit-Learn durchgeführt. Es werden $25\%$ der Daten dem Testdatensatz zugesprochen. Dieser Datensatz wird während dem gesamten Training nicht beachtet. Anhand dem Testdatensatz werden am Ende das optimierte Modell auf die während dem Training ungesehenen Daten evaluiert. Die Aufteilung wird mit dem oben definierten Seed durchgeführt, um die Ergebnisse reproduzierbar zu machen. Die Daten werden während der Aufteilung gemischt und statifiziert getrennt. Stratifizierte Aufteilung bedeutet, dass sich sowohl in dem Trainings- als auch im Testdatensatz die gleiche prozentuale Aufteilung der Zielvariable Weinqualität befinden.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df.index, df['quality'], test_size=0.25, random_state=seed, shuffle=True, stratify=df['quality'])

train_df = df.iloc[X_train]
test_df = df.iloc[X_test]

train_df

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	alcohol_Logarithm	residual sugar_Logarithm
2181	7.0	0.16	0.26	7.3	0.047	30.0	220.0	0.99622	3.38	0.58	10.1	6	2.312535	1.987874
3927	7.2	0.22	0.35	5.5	0.054	37.0	183.0	0.99474	3.08	0.50	10.3	5	2.332144	1.704748
728	8.1	0.17	0.44	14.1	0.053	43.0	145.0	1.00060	3.28	0.75	8.8	8	2.174752	2.646175
4173	6.4	0.24	0.27	1.5	0.040	35.0	105.0	0.98914	3.13	0.30	12.4	6	2.517696	0.405465
2726	6.0	0.32	0.30	1.3	0.025	18.0	112.0	0.98802	3.07	0.64	13.3	7	2.587764	0.262364
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2406	6.8	0.19	0.23	5.1	0.034	71.0	204.0	0.99420	3.23	0.69	10.1	5	2.312535	1.629241
2260	6.3	0.18	0.22	1.5	0.043	45.0	155.0	0.99238	3.19	0.48	10.2	5	2.322388	0.405465
1927	7.1	0.22	0.32	16.9	0.056	49.0	158.0	0.99980	3.37	0.38	9.6	6	2.261763	2.827314
2855	5.9	0.33	0.32	8.1	0.038	9.0	34.0	0.99110	3.22	0.36	12.7	7	2.541602	2.091864
617	6.8	0.25	0.34	14.0	0.032	47.0	133.0	0.99520	3.37	0.50	12.2	7	2.501436	2.639057

3376 rows × 14 columns

test_df

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	alcohol_Logarithm	residual sugar_Logarithm
2632	6.2	0.21	0.26	13.10	0.050	59.0	150.0	0.99772	3.31	0.46	9.0	5	2.197225	2.572612
2665	7.1	0.27	0.28	1.25	0.023	3.0	89.0	0.98993	2.95	0.30	11.4	4	2.433613	0.223144
2150	7.6	0.48	0.37	1.20	0.034	5.0	57.0	0.99256	3.05	0.54	10.4	3	2.341806	0.182322
3701	7.4	0.16	0.27	15.50	0.050	25.0	135.0	0.99840	2.90	0.43	8.7	7	2.163323	2.740840
3104	7.7	0.46	0.18	3.30	0.054	18.0	143.0	0.99392	3.12	0.51	10.8	6	2.379546	1.193922
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1017	6.3	0.23	0.21	5.10	0.035	29.0	142.0	0.99420	3.36	0.33	10.1	7	2.312535	1.629241
583	6.2	0.24	0.29	13.30	0.039	49.0	130.0	0.99520	3.33	0.46	11.0	8	2.397895	2.587764
1664	7.0	0.24	0.25	1.70	0.042	48.0	189.0	0.99200	3.25	0.42	11.4	6	2.433613	0.530628
1112	6.8	0.18	0.37	1.50	0.027	37.0	93.0	0.99200	3.30	0.45	10.8	6	2.379546	0.405465
4416	6.0	0.35	0.46	0.90	0.033	9.0	65.0	0.98934	3.24	0.35	12.1	4	2.493205	-0.105361

1126 rows × 14 columns

Speichern des Datensatzes

train_df.to_csv("data/train_weisswein_preprocessed.csv",sep=';',index=False)
test_df.to_csv("data/test_weisswein_preprocessed.csv",sep=';',index=False)

Datenanalyse

Korrelationsmatrix

Mit Hilfe der Korrelationsmatrix lassen sich Korrelationen zwischen den Features und den Features mit der Zielvariablen darstellen. Mit Blick auf die Zielvariable "quality" erkennt man eine moderate Korrelation zu den Features "alcohol", "density" und "chlorides". Daraus lässt sich herleiten, dass diese Features einen höheren Einfluss auf die Vorhersage unserer Modelle nehmen werden. Allerdings sollte man bei einigen Algorithmen Korrelationen zwischen Features vermeiden. Multikollinearität zwischen Features macht die Schätzung des Regressionskoeffizienten instabil und sie wird ungenauer.

Quellen:

• https://de.wikipedia.org/wiki/Multikollinearit%C3%A4t
• https://magoosh.com/statistics/pearson-correlation-coefficient/
• https://www.accountingcoach.com/blog/what-is-the-coefficient-of-correlation

correlation_matrix = train_df.corr()
correlation_matrix

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	alcohol_Logarithm	residual sugar_Logarithm
fixed acidity	1.000000	-0.046495	0.289624	0.076430	0.082431	-0.026143	0.067661	0.253869	-0.394528	-0.028987	-0.113803	-0.088480	-0.109349	0.059403
volatile acidity	-0.046495	1.000000	-0.160668	0.057252	0.017185	-0.071575	0.098533	-0.010702	-0.033185	-0.035661	0.084213	-0.128470	0.070793	0.101971
citric acid	0.289624	-0.160668	1.000000	0.087733	0.036582	0.127887	0.124891	0.138359	-0.152483	0.067582	-0.060464	0.006184	-0.062888	0.072569
residual sugar	0.076430	0.057252	0.087733	1.000000	0.255597	0.343204	0.410051	0.838573	-0.195250	-0.018294	-0.478968	-0.110791	-0.493436	0.938769
chlorides	0.082431	0.017185	0.036582	0.255597	1.000000	0.121873	0.332957	0.469893	-0.031905	0.079481	-0.518506	-0.313374	-0.521323	0.223371
free sulfur dioxide	-0.026143	-0.071575	0.127887	0.343204	0.121873	1.000000	0.622337	0.343954	-0.008545	0.079742	-0.269085	0.015068	-0.274254	0.347809
total sulfur dioxide	0.067661	0.098533	0.124891	0.410051	0.332957	0.622337	1.000000	0.547046	0.024808	0.159143	-0.461150	-0.171947	-0.466925	0.422419
density	0.253869	-0.010702	0.138359	0.838573	0.469893	0.343954	0.547046	1.000000	-0.081709	0.092243	-0.815035	-0.318027	-0.820908	0.772972
pH	-0.394528	-0.033185	-0.152483	-0.195250	-0.031905	-0.008545	0.024808	-0.081709	1.000000	0.169218	0.101405	0.100540	0.110064	-0.170065
sulphates	-0.028987	-0.035661	0.067582	-0.018294	0.079481	0.079742	0.159143	0.092243	0.169218	1.000000	-0.046919	0.036664	-0.044199	-0.021165
alcohol	-0.113803	0.084213	-0.060464	-0.478968	-0.518506	-0.269085	-0.461150	-0.815035	0.101405	-0.046919	1.000000	0.452146	0.998319	-0.410607
quality	-0.088480	-0.128470	0.006184	-0.110791	-0.313374	0.015068	-0.171947	-0.318027	0.100540	0.036664	0.452146	1.000000	0.447331	-0.069520
alcohol_Logarithm	-0.109349	0.070793	-0.062888	-0.493436	-0.521323	-0.274254	-0.466925	-0.820908	0.110064	-0.044199	0.998319	0.447331	1.000000	-0.423633
residual sugar_Logarithm	0.059403	0.101971	0.072569	0.938769	0.223371	0.347809	0.422419	0.772972	-0.170065	-0.021165	-0.410607	-0.069520	-0.423633	1.000000

plot_correlationmatrix(train_df, threshold = 0.3)

correlation_matrix['quality'].sort_values(ascending=False)

quality                     1.000000
alcohol                     0.452146
alcohol_Logarithm           0.447331
pH                          0.100540
sulphates                   0.036664
free sulfur dioxide         0.015068
citric acid                 0.006184
residual sugar_Logarithm   -0.069520
fixed acidity              -0.088480
residual sugar             -0.110791
volatile acidity           -0.128470
total sulfur dioxide       -0.171947
chlorides                  -0.313374
density                    -0.318027
Name: quality, dtype: float64

Auswahl von Features anhand der Korrelationsmatrix

Zur Minimierung des Problems der Multikollinearität werden einige Features eliminiert. Man erkennt, dass das Feature "alcohol", bzw dessen logarithmierte Form die größte Korrelation zur Zielvariable besitzt. Als Schwellenwert der Korrelation zwischen Features wurde 0.3 gewählt. Durch die Wahl des Features "alcohol" elimieren wir dadurch die Features "total sulfure dioxide", "residual sugar" und dessen logarithmierte Form, "chlorides" und "density". Die nächstgrößte Korrelation der verbleibenden Features finde sich in "volatile acidity". Dieses Feature besitzt keine Korrelationen über unserem Schwellenwert, wodurch keine anderen Features eliminiert werden. Nach diesem Schema wurden die Features "pH", "sulphates" und "citric acid" gewählt und das Feature "fixed acidity" ebenfalls eliminiert.

Daraus erfolgt die Liste folgender Features für den Datensatz der Feature Selection:

• "alcohol" / "alcohol_Logarithm"
• "volatile acidity"
• "pH"
• "sulphates"
• "citric acid"

Im Laufe des Projekts wird sowohl ein SVR Modell mit dem gesamten Datensatz und ein SVR Modell mit dem Datensatz der Feature Selection trainiert und anschließend evaluiert.

Darstellung der Features mit höchster Korrelation untereinander

plot_correlatedfeatures()

Darstellung des Features mit höchster Korrelation zur Zielvariablen:

Eine mäßig-starke Korrelation hat das Feature Alkohol zur Zielvariablen Qualität. Dies wirkt sich positiv auf das spätere Modell aus, da das Feature dadurch einen besseren Informationsgewinn hat.

sns.lmplot(x="quality", y="alcohol", data=df, line_kws={'color': 'grey'})

<seaborn.axisgrid.FacetGrid at 0x272ff206cd0>

Funktion zum Laden der Datensätze

def get_datasets(logarithm = False, feature_selection = False):
    """Laden der Datensätze
    
    Args:
        logarithm (bool): Logarithmierte Features anstelle der normalen verwenden
        feature_selection (bool): Nur selektierte Features des Datensatzes laden
        
    Returns:
        train_df (pd.DataFrame): Trainingsdatensatz
        test_df (pd.DataFrame): Testdatensatz
    """
    train_df = pd.read_csv('data/train_weisswein_preprocessed.csv',sep=";")
    test_df = pd.read_csv('data/test_weisswein_preprocessed.csv',sep=";")
    
    if feature_selection:
        columns = ["alcohol", "alcohol_Logarithm", "pH", "volatile acidity", "sulphates", "citric acid", "quality"]
        
        if logarithm:
            columns.remove("alcohol")
            
        else:
            columns.remove("alcohol_Logarithm")
        
    else:
        columns = list(train_df.columns)
        
        if logarithm:
            columns.remove("alcohol")
            columns.remove("residual sugar")
            
        else:
            columns.remove("alcohol_Logarithm")
            columns.remove("residual sugar_Logarithm")
        
    return train_df[columns], test_df[columns]
        

Skalierung

In Machine Learning ist die Skalierung von Daten ein wichtiger Schritt. Einige Machine Learning Algorithmen performen besser, wenn sie sie eine ähnliche Größenordnung der Einheiten vorweisen. Dies liegt an der Schrittgröße, die im Minimierungsverfahren gebraucht wird. Bei unterschiedlicher Größenordnung würde man pro Einheit eine Schrittgröße benötigen. Als Skaliermethoden werden meist eine MinMax-Skalierung oder eine Skalierung anhand der Standardabweichung bevorzugt angewandt. Dabei bietet es sich an, die Funktionen StandardScaler() und MinMaxScaler() von Scikit-Learn zu verwenden. Diese zwei Skaliermethoden werden im Laufe des Jupyter Notebooks verglichen, indem sie für den Support Vector Regressor als Hyperparameter mitgegeben werden. Beim Random Forest Regressor macht eine Skalierung keinen Sinn, da dies kein distanzbasiertes Modell ist.

Die Skalierung wird pro Training in der Kreuzvalidierung durchgeführt. So wird bei jedem neuen Training zunächst die Scikit-Learn-Funktionen mit den Trainingsdaten trainiert und anschließend der Trainings- und Validierungsdatensatz mit den Scikit-Learn-Funktionen skaliert.

Quellen:

• Machine Learning for Intelligence Systems Prof. Dr. Markus Schneider
• https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35

Machine Learning Modelle

Da die Aufgabe - die Bestimmung der Weinqualität - eine ordinale Regression ist, werden Regressionsalgorithmen benötigt.

Support Vector Regressor Trainingsfunktion

Eins der Machine Learning Modelle, welche wir verwenden ist ein Support Vektor Regressor(SVR). Es ist eine Version, der in der Vorlesung behandelten Support Vektor Maschine. Diese Version wird bei Regressionsproblemen angewandt.

Mit 10.000 Datenpunkte gilt ist Datensatz recht klein und somit kann die libsvm Implementation ohne Bedenken verwendet werden. Diese Implementierung bietet im Gegensatz zur linearen SVR verschiedene Kernel. Bei größeren Datensätzen mit mehreren zehntausend Datenpunkten steigt die Komplexität mehr als quadratisch an.

Da die Scikit-Learn-Implementierung von SVR eine Vielzahl an Parametern besitzt, müssen die korrekten Parameter zunächst mit einem Trainingprozess bestimmt werden.

Die Suche der besten Parameterkombination erfolgt mittels Gridsearch. Da Gridsearch eine Komplexität von $\mathcal{O}(k^n)$ Möglichkeiten besitzt, muss vorab eine Auswahl getroffen werden, welche Parameter ausführlich optimiert werden.

SVR selbst besitzt nur zwei freie Parameter.

• Epsilon, welches das Epsilon Band definiert, in welchem falsch bewertete Datenpunkte nicht bestraft werden.
• C, der Regulisierungsparameter. Die Stärke der Regularisierung ist invers proportional zu C.

Nach vorherigen Tests stellten sich die Daten nicht als linear seperabel heraus. Diese Implemenation verwendet den Radial Bias Kernel, kurz rbf, da dieser gute Ergebnisse lieferte. Es wurde sich gegen den Polynomial Kernel entschieden, da dieser schlechtere Ergebnisse bei erhöhter Laufzeit lieferte.

Der Radial Bias Kernel besitzt eine zusätliche Variable Gamma, welche optimiert werden kann. Standardmäßig wird Gamma mit Hilfe der Trainingsdaten bestimmt.

• Scale, gamma = $\frac{1}{n\_features*X.var()}$
• Auto, gamma = $\frac{1}{n\_features}$

In den neueren Versionen ist der Standardwert für Gamma auf "scale" gesetzt.

Ein weiterer Optimierungsparameter ist das oben genannte Skalieren. Dies wird mittels einer Pipeline umgesetzt.

Daraus ergeben sich vier Parameter welche mit Hilfe des Gridsearchalgorithmus optimiert werden müssen. Die Funktion kann ebenso mit anderen Kerneln verwendet werden, zum Beispiel "sigmoid", "linear" oder "poly". Der Polynomial Kernel besitzt einen eigenen Parameter Grad("degree"), welche anstelle von Gamma optimiert werden würde.

Quellen: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html

def SupportVectorRegressor_train(X, y, cross_validation = False, hyperparameter = None, n_splits = 3, n_repeats = 1, seed = 42, kernel = "rbf", test_for_professor = False):
    """Trainiert ein SVR Model mit gegebenen Hyperparametern. 
    Falls Cross Validation auf Wahr gesetzt wurde, werden SVR Modelle mit Hilfe einer 
    Datensatzaufteilung durch KFold trainiert um optimale Hyperparameter zu bestimmen.
    
    Args:
        X (np.array): Features des Trainingsdatensatzes
        y (np.array): Labels des Trainingsdatensatz
        cross_validation (bool): Ob Cross Validation verwendet werden soll um Hyperparameter zu bestimmen, 
                                ansonsten wird ein Modell mit den übergebenen Hyperparametern trainiert. Defaults ist False
        hyperparameter (dict oder None): Übergabe der Hyperparameter für das Modell bei cross_validation = True. Defaults ist None
        n_splits (int): Anzahl an Spaltungen für Cross Validation. Defaults ist 3
        n_repeats (n_repeats): Wiederholungen der Cross Validation. Defaults ist 1
        seed (int): Seed für den KFold Algorithmus. Defaults ist 42
        kernel (str): Kernel, welcher von dem SVR Modell verwendet werden soll. Defaults ist "rbf"
        test_for_professor (bool): Test für den Professor/Kommilitonen um schneller zu trainieren. Defaults ist False
        
    Returns:
        cross_validation == True:
            train_config (dict): Ein Array von Objekten, welcher die Hyperparameter und Metriken des Trainings beinhaltet
        cross_validation == False:
            model (object): Das trainierte Model
                
    Raise: 
        ValueError: Falls beim Trainieren eines Modells in dem Hyperparameter Dictionary ein falscher Wert übergeben wurde.
    """
    if cross_validation:
        zeitanfang = time.time()
        train_config = {    
        "kernel":[],
        "epsilon":[],
        "C":[],
        "gamma":[],
        "degree":[],
        "r2score_train":[],
        "mse_train":[],
        "acc_train":[],
        "r2score_val":[],
        "mse_val":[],
        "acc_val":[],
        "scaler":[],
        "time":[],
        }
        
        min_samples_split=np.unique(np.round(np.logspace(1, 6, num=10, base=2)).astype(int))
        
        
        # Default Values, please change
        epsilon = np.arange(0.05, 0.5, 0.05)
        C = np.logspace(-1.5, 1.5, num=30, base=10)
        
        # Manual gamma settings, with auto and scale as the first parameters
        # scale uses 1 / (n_features * X.var())
        # auto uses 1 / n_features.
        gamma = np.logspace(-2, 0, num=5, base=10)
        gamma = ["scale", "auto"]+list(gamma)
        
        # Degree range for use with the poly kernel. The second variable in range is exclusive.
        degree = range(1,4)

        if test_for_professor:
            # Test values to test functionality
            epsilon = np.arange(0.05, 0.1, 0.05)
            C = np.logspace(0, 0.5, num=5, base=10)
            gamma = [0.31622776601683794]
            degree = [3]
        
        # Only the poly kernel uses the degree parameter, so for all other kernels it can be set to one element to reduce permutations.
        if kernel is not "poly":
            degree=[0]
        
        # linear does not use the gamma parameter, so it can be reduced to one entry to reduce permutations.
        if kernel is "linear":
            gamma = ["scale"]
        
        
        scaler = [StandardScaler(),MinMaxScaler()]
        
        cvvr = CrossValidationValueRecorder()
        
        rkf = RepeatedKFold(n_splits=n_splits, n_repeats=n_repeats, random_state=seed)
        
        # Calculating the amount of the length of the cartesian product
        num_train = ProductLength([epsilon, C, gamma, scaler, degree])
        
        # Running the cross validation
        for i,(e,c,g,s,d) in enumerate(itertools.product(epsilon,C,gamma,scaler,degree)):
            print(f'{i+1:5d}/{num_train} {e:.3f} {c:.3f} {g} {str(s):16}', d, end='')
            
            # Insert model specific values
            train_config["epsilon"].append(e)
            train_config["C"].append(c)
            train_config["gamma"].append(g)
            train_config["scaler"].append(s)
            train_config["degree"].append(d)
            train_config["kernel"].append(kernel)
            cvvr.Reset()
            
            trainingTimeBegin = time.time()
            for train_index, val_index in rkf.split(X):
                X_train, X_val = X[train_index], X[val_index] 
                y_train, y_val = y[train_index], y[val_index]
                
                svr = SVR(kernel=kernel, C=c, epsilon=e, gamma=g, degree=d)
                pipe = Pipeline([('scaler', s), ('svc', svr)])
                pipe.fit(X_train,y_train)
                
                model = pipe.fit(X_train, y_train)

                #val
                y_pred = model.predict(X_val)
                cvvr.AddValidationMetric(y_val, y_pred)
                #Train
                y_pred = model.predict(X_train)
                cvvr.AddTrainMetric(y_train, y_pred)
            
            trainingTime = time.time()-trainingTimeBegin
            train_config["time"].append(trainingTime)
            print(' ->',cvvr.GetMSE())
            # Add Scores to evaluate
            cvvr.UpdateConfig(train_config)
        
        processingTime = time.time()-zeitanfang
        print(f"Time Taken {processingTime:.3f}s")
        return train_config
    else:
        if (hyperparameter['gamma'] == "auto" or hyperparameter["gamma"] == "scale"):
            gamma = hyperparameter['gamma']
        else:
            gamma = float(hyperparameter['gamma'])
        
        svr = SVR(kernel = kernel, degree = hyperparameter['degree'], C = hyperparameter['C'], epsilon = hyperparameter['epsilon'], gamma = gamma)
        
        if hyperparameter['scaler'] == "StandardScaler()":
            scaler = StandardScaler()
        elif hyperparameter['scaler'] == "MinMaxScaler()":
            scaler =  MinMaxScaler()
        else:
            raise ValueError(f"Scaler not found please insert \"StandardScaler()\" or \"MinMaxScaler()\" ")
        
        pipe = Pipeline([('scaler', scaler), ('svc', svr)])
        model = pipe.fit(X, y)
        
        return model

Random Forest Regressor Trainingsfunktion

Entscheidungsbäume sind in der Lage sich sehr gut auf einen Datensatz anzupassen, allerdings weisen sie sehr oft eine hohe Varianz auf und neigen daher sehr stark zum Overfitting. Daher wurde als zweites Modell der Random Forest Regressor gewählt. Dieser besteht aus mehreren unkorrelierten Entscheidungsbäumen. Dadurch wird das Overfitting von Entscheidungsbäumen vermieden und somit eine bessere Generalisierung ermöglicht.

Für das Modell wird der Random Forest Regressor von Scikit-Learn implementiert. Dieser bietet eine Reihe von Parametern welche in der Hyperparameteroptimierung angepasst werden können. Die optimalen Parameter werden ebenfalls mit dem Gridsearchalgorithmus ermittelt.

Um der Komplexität des Gridsearchalgorithmus entgegenzuwirken werden Parameter ausgewählt, welche ausführlich optimiert werden. Zuvor wurden durch Tests Parameter ermittelt, welche eine signifikante Änderung für das Training bedeuteten. Parameter, welche eine starke Korrelation aufweisen (zb. min_samples_split und min_samples_leaf) haben einen sehr ähnlichen Effekt auf das Training, weshalb lediglich einer dieser Parameter optimiert wird.

Aus diesen Gründen werden folgende Parameter in der Hyperparameteroptimierung angewandt:

• max_depth: Maximale Tiefe der Bäume
• min_samples_split: Minimale Anzahl an Datenpunkten, um eine Aufteilung durchgeführen zu dürfen.
• max_features: Anzahl an Features, die für eine Aufteilung betrachtet werden

Die anderen Parameter werden auf Default gesetzt oder auf Werte, die bei zuvor durchgeführten Tests am sinnvollsten erschienen.

def RandomForestRegressor_train(X, y, cross_validation=False, hyperparameter=None, n_splits=3, n_repeats=1, seed=42, test_for_professor = False): 
    """Trainiert ein Random Forest Regressor Model mit gegebenen Hyperparametern. 
    Falls Cross Validation auf Wahr gesetzt wurde, werden RFR Modelle mit Hilfe einer 
    Datensatzaufteilung durch KFold trainiert um optimale Hyperparameter zu bestimmen.
    
    Args:
        X (np.array): Features des Trainingsdatensatzes
        y (np.array): Labels des Trainingsdatensatz
        cross_validation (bool): Ob Cross Validation verwendet werden soll um Hyperparameter zu bestimmen, 
                                ansonsten wird ein Modell mit den übergebenen Hyperparametern trainiert. Defaults ist False
        hyperparameter (dict oder None): Übergabe der Hyperparameter für das Modell bei cross_validation = True. Defaults ist None
        n_splits (int): Anzahl an Spaltungen für Cross Validation. Defaults ist 3
        n_repeats (n_repeats): Wiederholungen der Cross Validation. Defaults ist 1
        seed (int): Seed für den KFold Algorithmus. Defaults ist 42
        test_for_professor (bool): Test für den Professor/Kommilitonen um schneller zu trainieren. Defaults ist False
        
    Returns:
        cross_validation == True:
            train_config (dict): Ein Array von Objekten, welcher die Hyperparameter und Metriken des Trainings beinhaltet
        cross_validation == False:
            model (object): Das trainierte Model
                
    Raise: 
        ValueError: Falls beim Trainieren eines Modells in dem Hyperparameter Dictionary ein falscher Wert übergeben wurde.
    """
    
    if cross_validation:
        zeitanfang = time.time()
        
        train_config = {    
        "n_estimators":[],
        "max_depth":[],
        "min_samples_split":[],
        "min_weight_fraction_leaf":[],
        "ccp_alpha":[],
        "oob_score":[],
        "max_features":[],
        "r2score_train":[],
        "mse_train":[],
        "acc_train":[],
        "r2score_val":[],
        "mse_val":[],
        "acc_val":[],
        "time":[],
        }
        
        n_estimators=[1000]
        max_depth= [i for i in range(1,30,1)]
        min_samples_split=np.unique(np.round(np.logspace(1, 6, num=10, base=2)).astype(int))
        min_weight_fraction_leaf=[0]#is not optimized for time reasons. However, zero is usually best.
        oob_score=[True]#,False] 
        ccp_alpha=[0] #is not optimized for time reasons. However, zero is usually best.
        max_features=['auto', 'sqrt','log2']
        
        if test_for_professor:
            #Test for fast run 
            n_estimators=[1000]
            max_depth=[20,25]
            min_samples_split=[2,4]
            min_weight_fraction_leaf=[0]
            oob_score=[False] 
            ccp_alpha=[0]
            max_features=['auto', 'sqrt']

        cvvr = CrossValidationValueRecorder()
        
        rkf = RepeatedKFold(n_splits=n_splits, n_repeats=n_repeats, random_state=seed)
        
        num_train = ProductLength([n_estimators, max_depth, min_samples_split, min_weight_fraction_leaf,oob_score, ccp_alpha, max_features])
        
        print(f'Step/Maxsteps | Estimator | MaxDepth | MinSampleSplit | Minweightfractionleaf | oobscore | ccpalpha | max_features')
        
        for i,(n_e,md,ms,mw,oob,ccp,mf) in enumerate(itertools.product(n_estimators, max_depth, min_samples_split, min_weight_fraction_leaf, oob_score, ccp_alpha,max_features)):
            print(f'{i+1:5d}/{num_train} {n_e:5d} {md:2d} {ms:2d} {mw:.3f} {oob:.3f} {ccp:.3f} {mf}', end='')

            train_config["n_estimators"].append(n_e)
            train_config["max_depth"].append(md)
            train_config["min_samples_split"].append(ms)
            train_config["min_weight_fraction_leaf"].append(mw)
            train_config["oob_score"].append(oob)
            train_config["ccp_alpha"].append(ccp)
            train_config["max_features"].append(mf)
            
            cvvr.Reset()
            
            trainingTimeBegin = time.time()
            for train_index, val_index in rkf.split(X):
                X_train, X_val = X[train_index], X[val_index] 
                y_train, y_val = y[train_index], y[val_index]
                rfr=RandomForestRegressor(max_depth=md, min_samples_split=ms, min_weight_fraction_leaf=mw, oob_score=oob, ccp_alpha=ccp, random_state=seed, n_jobs=-1, n_estimators=n_e, max_features=mf)
                model = rfr.fit(X_train, y_train)
                
                #val
                y_pred = model.predict(X_val)
                cvvr.AddValidationMetric(y_val, y_pred)
                #Train
                y_pred = model.predict(X_train)
                cvvr.AddTrainMetric(y_train, y_pred)
            
            trainingTime = time.time()-trainingTimeBegin
            train_config["time"].append(trainingTime)
            print(' ->',cvvr.GetMSE())
            # Add Scores to evaluate
            cvvr.UpdateConfig(train_config)
        
        processingTime = time.time()-zeitanfang
        print(f"Time Taken {processingTime:.3f}s")
        return  train_config
    else:
        rfr=RandomForestRegressor(random_state=seed, n_jobs=-1, n_estimators=hyperparameter['n_estimators'], 
                                  max_depth=hyperparameter['max_depth'],min_samples_split=hyperparameter['min_samples_split'], 
                                   min_weight_fraction_leaf=hyperparameter['min_weight_fraction_leaf'],oob_score=hyperparameter['oob_score'],
                                   ccp_alpha=hyperparameter['ccp_alpha'], max_features=hyperparameter['max_features'])
        model = rfr.fit(X, y)
        return model

Hyperparametersuche der Modelle

Ausführung der Trainingsfunktionen der Modelle. Nach jedem Modelldurchlauf werden die Ergebnisse in eine .csv Datei zwischengespeichert.

Hyperparametersuche Random Forest Regressor

train_df, test_df = get_datasets(logarithm=True, feature_selection=False)

X_train = train_df.drop("quality",axis=1).values
X_test = test_df.drop("quality",axis=1).values

y_train = train_df["quality"].values
y_test = test_df["quality"].values

train_config = RandomForestRegressor_train(X_train,y_train,cross_validation=True, test_for_professor = test_for_professor)

df = pd.DataFrame.from_dict(train_config)
df = df.sort_values(by=['mse_val'])
if not test_for_professor:
    df.to_csv('results/train_conf_tree.csv',index=False)

Step/Maxsteps | Estimator | MaxDepth | MinSampleSplit | Minweightfractionleaf | oobscore | ccpalpha | max_features
    1/870  1000  1  2 0.000 1.000 0.000 auto -> 0.6246662850329954
    2/870  1000  1  2 0.000 1.000 0.000 sqrt -> 0.6494371022085937
    3/870  1000  1  2 0.000 1.000 0.000 log2 -> 0.6494371022085937
    4/870  1000  1  3 0.000 1.000 0.000 auto -> 0.6246662850329954
    5/870  1000  1  3 0.000 1.000 0.000 sqrt -> 0.6494371022085937
    6/870  1000  1  3 0.000 1.000 0.000 log2 -> 0.6494371022085937
    7/870  1000  1  4 0.000 1.000 0.000 auto -> 0.6246662850329954
    8/870  1000  1  4 0.000 1.000 0.000 sqrt -> 0.6494371022085937
    9/870  1000  1  4 0.000 1.000 0.000 log2 -> 0.6494371022085937
   10/870  1000  1  6 0.000 1.000 0.000 auto -> 0.6246662850329954
   11/870  1000  1  6 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   12/870  1000  1  6 0.000 1.000 0.000 log2 -> 0.6494371022085936
   13/870  1000  1  9 0.000 1.000 0.000 auto -> 0.6246662850329954
   14/870  1000  1  9 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   15/870  1000  1  9 0.000 1.000 0.000 log2 -> 0.6494371022085937
   16/870  1000  1 14 0.000 1.000 0.000 auto -> 0.6246662850329955
   17/870  1000  1 14 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   18/870  1000  1 14 0.000 1.000 0.000 log2 -> 0.6494371022085937
   19/870  1000  1 20 0.000 1.000 0.000 auto -> 0.6246662850329954
   20/870  1000  1 20 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   21/870  1000  1 20 0.000 1.000 0.000 log2 -> 0.6494371022085935
   22/870  1000  1 30 0.000 1.000 0.000 auto -> 0.6246662850329954
   23/870  1000  1 30 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   24/870  1000  1 30 0.000 1.000 0.000 log2 -> 0.6494371022085937
   25/870  1000  1 44 0.000 1.000 0.000 auto -> 0.6246662850329955
   26/870  1000  1 44 0.000 1.000 0.000 sqrt -> 0.6494371022085937
   27/870  1000  1 44 0.000 1.000 0.000 log2 -> 0.6494371022085935
   28/870  1000  1 64 0.000 1.000 0.000 auto -> 0.6246662850329954
   29/870  1000  1 64 0.000 1.000 0.000 sqrt -> 0.6494371022085935
   30/870  1000  1 64 0.000 1.000 0.000 log2 -> 0.6494371022085935
   31/870  1000  2  2 0.000 1.000 0.000 auto -> 0.5569543188751295
   32/870  1000  2  2 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   33/870  1000  2  2 0.000 1.000 0.000 log2 -> 0.5935648257281153
   34/870  1000  2  3 0.000 1.000 0.000 auto -> 0.5569543188751295
   35/870  1000  2  3 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   36/870  1000  2  3 0.000 1.000 0.000 log2 -> 0.5935648257281153
   37/870  1000  2  4 0.000 1.000 0.000 auto -> 0.5569543188751295
   38/870  1000  2  4 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   39/870  1000  2  4 0.000 1.000 0.000 log2 -> 0.5935648257281153
   40/870  1000  2  6 0.000 1.000 0.000 auto -> 0.5569543188751295
   41/870  1000  2  6 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   42/870  1000  2  6 0.000 1.000 0.000 log2 -> 0.5935648257281153
   43/870  1000  2  9 0.000 1.000 0.000 auto -> 0.5569543188751295
   44/870  1000  2  9 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   45/870  1000  2  9 0.000 1.000 0.000 log2 -> 0.5935648257281153
   46/870  1000  2 14 0.000 1.000 0.000 auto -> 0.5569543188751295
   47/870  1000  2 14 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   48/870  1000  2 14 0.000 1.000 0.000 log2 -> 0.5935648257281154
   49/870  1000  2 20 0.000 1.000 0.000 auto -> 0.5569543188751295
   50/870  1000  2 20 0.000 1.000 0.000 sqrt -> 0.5935648257281153
   51/870  1000  2 20 0.000 1.000 0.000 log2 -> 0.5935648257281153
   52/870  1000  2 30 0.000 1.000 0.000 auto -> 0.5569543188751295
   53/870  1000  2 30 0.000 1.000 0.000 sqrt -> 0.5935590171994316
   54/870  1000  2 30 0.000 1.000 0.000 log2 -> 0.5935590171994317
   55/870  1000  2 44 0.000 1.000 0.000 auto -> 0.5569543188751295
   56/870  1000  2 44 0.000 1.000 0.000 sqrt -> 0.5936038077268836
   57/870  1000  2 44 0.000 1.000 0.000 log2 -> 0.5936038077268836
   58/870  1000  2 64 0.000 1.000 0.000 auto -> 0.5569543188751295
   59/870  1000  2 64 0.000 1.000 0.000 sqrt -> 0.5936742028265631
   60/870  1000  2 64 0.000 1.000 0.000 log2 -> 0.5936742028265631
   61/870  1000  3  2 0.000 1.000 0.000 auto -> 0.5298336477485531
   62/870  1000  3  2 0.000 1.000 0.000 sqrt -> 0.5552120953049079
   63/870  1000  3  2 0.000 1.000 0.000 log2 -> 0.5552120953049079
   64/870  1000  3  3 0.000 1.000 0.000 auto -> 0.5298336477485531
   65/870  1000  3  3 0.000 1.000 0.000 sqrt -> 0.5552249795056975
   66/870  1000  3  3 0.000 1.000 0.000 log2 -> 0.5552249795056975
   67/870  1000  3  4 0.000 1.000 0.000 auto -> 0.5298336477485532
   68/870  1000  3  4 0.000 1.000 0.000 sqrt -> 0.5552074660107625
   69/870  1000  3  4 0.000 1.000 0.000 log2 -> 0.5552074660107623
   70/870  1000  3  6 0.000 1.000 0.000 auto -> 0.529834059654752
   71/870  1000  3  6 0.000 1.000 0.000 sqrt -> 0.5551277008090799
   72/870  1000  3  6 0.000 1.000 0.000 log2 -> 0.5551277008090799
   73/870  1000  3  9 0.000 1.000 0.000 auto -> 0.5298313073854425
   74/870  1000  3  9 0.000 1.000 0.000 sqrt -> 0.5552345557030609
   75/870  1000  3  9 0.000 1.000 0.000 log2 -> 0.5552345557030608
   76/870  1000  3 14 0.000 1.000 0.000 auto -> 0.5298231498220131
   77/870  1000  3 14 0.000 1.000 0.000 sqrt -> 0.555318292304875
   78/870  1000  3 14 0.000 1.000 0.000 log2 -> 0.5553182923048751
   79/870  1000  3 20 0.000 1.000 0.000 auto -> 0.529810954144054
   80/870  1000  3 20 0.000 1.000 0.000 sqrt -> 0.5553943804176994
   81/870  1000  3 20 0.000 1.000 0.000 log2 -> 0.5553943804176994
   82/870  1000  3 30 0.000 1.000 0.000 auto -> 0.5298306660128583
   83/870  1000  3 30 0.000 1.000 0.000 sqrt -> 0.5554713333857669
   84/870  1000  3 30 0.000 1.000 0.000 log2 -> 0.5554713333857669
   85/870  1000  3 44 0.000 1.000 0.000 auto -> 0.5299803765691715
   86/870  1000  3 44 0.000 1.000 0.000 sqrt -> 0.5556215296398119
   87/870  1000  3 44 0.000 1.000 0.000 log2 -> 0.5556215296398119
   88/870  1000  3 64 0.000 1.000 0.000 auto -> 0.5301692839506301
   89/870  1000  3 64 0.000 1.000 0.000 sqrt -> 0.5559851151436761
   90/870  1000  3 64 0.000 1.000 0.000 log2 -> 0.5559851151436761
   91/870  1000  4  2 0.000 1.000 0.000 auto -> 0.5044670229959504
   92/870  1000  4  2 0.000 1.000 0.000 sqrt -> 0.5254048474104165
   93/870  1000  4  2 0.000 1.000 0.000 log2 -> 0.5254048474104164
   94/870  1000  4  3 0.000 1.000 0.000 auto -> 0.5044832121258706
   95/870  1000  4  3 0.000 1.000 0.000 sqrt -> 0.5255049656512056
   96/870  1000  4  3 0.000 1.000 0.000 log2 -> 0.5255049656512057
   97/870  1000  4  4 0.000 1.000 0.000 auto -> 0.5044902245225096
   98/870  1000  4  4 0.000 1.000 0.000 sqrt -> 0.5257212462164901
   99/870  1000  4  4 0.000 1.000 0.000 log2 -> 0.5257212462164901
  100/870  1000  4  6 0.000 1.000 0.000 auto -> 0.504541998916422
  101/870  1000  4  6 0.000 1.000 0.000 sqrt -> 0.5253781353601535
  102/870  1000  4  6 0.000 1.000 0.000 log2 -> 0.5253781353601535
  103/870  1000  4  9 0.000 1.000 0.000 auto -> 0.504502808896098
  104/870  1000  4  9 0.000 1.000 0.000 sqrt -> 0.5253807898939685
  105/870  1000  4  9 0.000 1.000 0.000 log2 -> 0.5253807898939685
  106/870  1000  4 14 0.000 1.000 0.000 auto -> 0.5045153096268097
  107/870  1000  4 14 0.000 1.000 0.000 sqrt -> 0.5255414638331574
  108/870  1000  4 14 0.000 1.000 0.000 log2 -> 0.5255414638331574
  109/870  1000  4 20 0.000 1.000 0.000 auto -> 0.504627200510556
  110/870  1000  4 20 0.000 1.000 0.000 sqrt -> 0.5258943898823267
  111/870  1000  4 20 0.000 1.000 0.000 log2 -> 0.5258943898823267
  112/870  1000  4 30 0.000 1.000 0.000 auto -> 0.5050124082578821
  113/870  1000  4 30 0.000 1.000 0.000 sqrt -> 0.5265696550658036
  114/870  1000  4 30 0.000 1.000 0.000 log2 -> 0.5265696550658036
  115/870  1000  4 44 0.000 1.000 0.000 auto -> 0.5059005664501636
  116/870  1000  4 44 0.000 1.000 0.000 sqrt -> 0.5271378517654594
  117/870  1000  4 44 0.000 1.000 0.000 log2 -> 0.5271378517654594
  118/870  1000  4 64 0.000 1.000 0.000 auto -> 0.5073593862001251
  119/870  1000  4 64 0.000 1.000 0.000 sqrt -> 0.5281195279313046
  120/870  1000  4 64 0.000 1.000 0.000 log2 -> 0.5281195279313046
  121/870  1000  5  2 0.000 1.000 0.000 auto -> 0.4834508473372005
  122/870  1000  5  2 0.000 1.000 0.000 sqrt -> 0.5021246824031157
  123/870  1000  5  2 0.000 1.000 0.000 log2 -> 0.5021246824031159
  124/870  1000  5  3 0.000 1.000 0.000 auto -> 0.4834628057753763
  125/870  1000  5  3 0.000 1.000 0.000 sqrt -> 0.502128030061811
  126/870  1000  5  3 0.000 1.000 0.000 log2 -> 0.502128030061811
  127/870  1000  5  4 0.000 1.000 0.000 auto -> 0.4835214827548278
  128/870  1000  5  4 0.000 1.000 0.000 sqrt -> 0.502209598038378
  129/870  1000  5  4 0.000 1.000 0.000 log2 -> 0.502209598038378
  130/870  1000  5  6 0.000 1.000 0.000 auto -> 0.48358503956146787
  131/870  1000  5  6 0.000 1.000 0.000 sqrt -> 0.5019889599148238
  132/870  1000  5  6 0.000 1.000 0.000 log2 -> 0.5019889599148236
  133/870  1000  5  9 0.000 1.000 0.000 auto -> 0.483654924694856
  134/870  1000  5  9 0.000 1.000 0.000 sqrt -> 0.5019237783745473
  135/870  1000  5  9 0.000 1.000 0.000 log2 -> 0.5019237783745473
  136/870  1000  5 14 0.000 1.000 0.000 auto -> 0.4839057051385467
  137/870  1000  5 14 0.000 1.000 0.000 sqrt -> 0.5025306851475689
  138/870  1000  5 14 0.000 1.000 0.000 log2 -> 0.5025306851475689
  139/870  1000  5 20 0.000 1.000 0.000 auto -> 0.48441149397869543
  140/870  1000  5 20 0.000 1.000 0.000 sqrt -> 0.5033117001036186
  141/870  1000  5 20 0.000 1.000 0.000 log2 -> 0.5033117001036186
  142/870  1000  5 30 0.000 1.000 0.000 auto -> 0.485740999887274
  143/870  1000  5 30 0.000 1.000 0.000 sqrt -> 0.5043063698906326
  144/870  1000  5 30 0.000 1.000 0.000 log2 -> 0.5043063698906326
  145/870  1000  5 44 0.000 1.000 0.000 auto -> 0.48786820132612974
  146/870  1000  5 44 0.000 1.000 0.000 sqrt -> 0.5059573838096176
  147/870  1000  5 44 0.000 1.000 0.000 log2 -> 0.5059573838096176
  148/870  1000  5 64 0.000 1.000 0.000 auto -> 0.49139999867464645
  149/870  1000  5 64 0.000 1.000 0.000 sqrt -> 0.5083795960342711
  150/870  1000  5 64 0.000 1.000 0.000 log2 -> 0.508379596034271
  151/870  1000  6  2 0.000 1.000 0.000 auto -> 0.4663477884909431
  152/870  1000  6  2 0.000 1.000 0.000 sqrt -> 0.4807797524535348
  153/870  1000  6  2 0.000 1.000 0.000 log2 -> 0.4807797524535349
  154/870  1000  6  3 0.000 1.000 0.000 auto -> 0.46641186778486504
  155/870  1000  6  3 0.000 1.000 0.000 sqrt -> 0.4807449353078023
  156/870  1000  6  3 0.000 1.000 0.000 log2 -> 0.4807449353078023
  157/870  1000  6  4 0.000 1.000 0.000 auto -> 0.4664371365554727
  158/870  1000  6  4 0.000 1.000 0.000 sqrt -> 0.48118057193966557
  159/870  1000  6  4 0.000 1.000 0.000 log2 -> 0.4811805719396656
  160/870  1000  6  6 0.000 1.000 0.000 auto -> 0.46664977000715974
  161/870  1000  6  6 0.000 1.000 0.000 sqrt -> 0.48113049866211716
  162/870  1000  6  6 0.000 1.000 0.000 log2 -> 0.48113049866211716
  163/870  1000  6  9 0.000 1.000 0.000 auto -> 0.4669263879254098
  164/870  1000  6  9 0.000 1.000 0.000 sqrt -> 0.4814768966484728
  165/870  1000  6  9 0.000 1.000 0.000 log2 -> 0.4814768966484728
  166/870  1000  6 14 0.000 1.000 0.000 auto -> 0.4678325949548899
  167/870  1000  6 14 0.000 1.000 0.000 sqrt -> 0.4821969638181569
  168/870  1000  6 14 0.000 1.000 0.000 log2 -> 0.4821969638181569
  169/870  1000  6 20 0.000 1.000 0.000 auto -> 0.4690013801034227
  170/870  1000  6 20 0.000 1.000 0.000 sqrt -> 0.48457314344077623
  171/870  1000  6 20 0.000 1.000 0.000 log2 -> 0.48457314344077623
  172/870  1000  6 30 0.000 1.000 0.000 auto -> 0.4715524051815676
  173/870  1000  6 30 0.000 1.000 0.000 sqrt -> 0.48685249056950797
  174/870  1000  6 30 0.000 1.000 0.000 log2 -> 0.48685249056950797
  175/870  1000  6 44 0.000 1.000 0.000 auto -> 0.4750632927646721
  176/870  1000  6 44 0.000 1.000 0.000 sqrt -> 0.48982648723748684
  177/870  1000  6 44 0.000 1.000 0.000 log2 -> 0.48982648723748684
  178/870  1000  6 64 0.000 1.000 0.000 auto -> 0.4804679990281362
  179/870  1000  6 64 0.000 1.000 0.000 sqrt -> 0.49428565157353116
  180/870  1000  6 64 0.000 1.000 0.000 log2 -> 0.49428565157353116
  181/870  1000  7  2 0.000 1.000 0.000 auto -> 0.4514614100649796
  182/870  1000  7  2 0.000 1.000 0.000 sqrt -> 0.4622448346267553
  183/870  1000  7  2 0.000 1.000 0.000 log2 -> 0.4622448346267553
  184/870  1000  7  3 0.000 1.000 0.000 auto -> 0.4513462508044291
  185/870  1000  7  3 0.000 1.000 0.000 sqrt -> 0.46241027247506045
  186/870  1000  7  3 0.000 1.000 0.000 log2 -> 0.46241027247506045
  187/870  1000  7  4 0.000 1.000 0.000 auto -> 0.45169722772176507
  188/870  1000  7  4 0.000 1.000 0.000 sqrt -> 0.4629825658720172
  189/870  1000  7  4 0.000 1.000 0.000 log2 -> 0.4629825658720172
  190/870  1000  7  6 0.000 1.000 0.000 auto -> 0.451922075692098
  191/870  1000  7  6 0.000 1.000 0.000 sqrt -> 0.4638180638617353
  192/870  1000  7  6 0.000 1.000 0.000 log2 -> 0.4638180638617353
  193/870  1000  7  9 0.000 1.000 0.000 auto -> 0.4527566769847464
  194/870  1000  7  9 0.000 1.000 0.000 sqrt -> 0.464515933962007
  195/870  1000  7  9 0.000 1.000 0.000 log2 -> 0.464515933962007
  196/870  1000  7 14 0.000 1.000 0.000 auto -> 0.4545514782773476
  197/870  1000  7 14 0.000 1.000 0.000 sqrt -> 0.466341249278812
  198/870  1000  7 14 0.000 1.000 0.000 log2 -> 0.466341249278812
  199/870  1000  7 20 0.000 1.000 0.000 auto -> 0.45689847778679743
  200/870  1000  7 20 0.000 1.000 0.000 sqrt -> 0.4688587090723996
  201/870  1000  7 20 0.000 1.000 0.000 log2 -> 0.4688587090723996
  202/870  1000  7 30 0.000 1.000 0.000 auto -> 0.46075576651889966
  203/870  1000  7 30 0.000 1.000 0.000 sqrt -> 0.4726876039553877
  204/870  1000  7 30 0.000 1.000 0.000 log2 -> 0.4726876039553876
  205/870  1000  7 44 0.000 1.000 0.000 auto -> 0.4659652839466588
  206/870  1000  7 44 0.000 1.000 0.000 sqrt -> 0.4782185727345909
  207/870  1000  7 44 0.000 1.000 0.000 log2 -> 0.4782185727345909
  208/870  1000  7 64 0.000 1.000 0.000 auto -> 0.4731371635743515
  209/870  1000  7 64 0.000 1.000 0.000 sqrt -> 0.48357382795534193
  210/870  1000  7 64 0.000 1.000 0.000 log2 -> 0.48357382795534193
  211/870  1000  8  2 0.000 1.000 0.000 auto -> 0.4375334090374429
  212/870  1000  8  2 0.000 1.000 0.000 sqrt -> 0.4471558697577829
  213/870  1000  8  2 0.000 1.000 0.000 log2 -> 0.4471558697577829
  214/870  1000  8  3 0.000 1.000 0.000 auto -> 0.4374494404687152
  215/870  1000  8  3 0.000 1.000 0.000 sqrt -> 0.44679165924633013
  216/870  1000  8  3 0.000 1.000 0.000 log2 -> 0.4467916592463302
  217/870  1000  8  4 0.000 1.000 0.000 auto -> 0.4375909807341605
  218/870  1000  8  4 0.000 1.000 0.000 sqrt -> 0.4475288457919054
  219/870  1000  8  4 0.000 1.000 0.000 log2 -> 0.4475288457919054
  220/870  1000  8  6 0.000 1.000 0.000 auto -> 0.43861061059188233
  221/870  1000  8  6 0.000 1.000 0.000 sqrt -> 0.44831571443783796
  222/870  1000  8  6 0.000 1.000 0.000 log2 -> 0.44831571443783796
  223/870  1000  8  9 0.000 1.000 0.000 auto -> 0.44020047474293406
  224/870  1000  8  9 0.000 1.000 0.000 sqrt -> 0.4509913844134506
  225/870  1000  8  9 0.000 1.000 0.000 log2 -> 0.4509913844134506
  226/870  1000  8 14 0.000 1.000 0.000 auto -> 0.44316298326806686
  227/870  1000  8 14 0.000 1.000 0.000 sqrt -> 0.453395793578989
  228/870  1000  8 14 0.000 1.000 0.000 log2 -> 0.453395793578989
  229/870  1000  8 20 0.000 1.000 0.000 auto -> 0.4466454769755462
  230/870  1000  8 20 0.000 1.000 0.000 sqrt -> 0.45743504340536933
  231/870  1000  8 20 0.000 1.000 0.000 log2 -> 0.45743504340536933
  232/870  1000  8 30 0.000 1.000 0.000 auto -> 0.4522662170615552
  233/870  1000  8 30 0.000 1.000 0.000 sqrt -> 0.46261396821429107
  234/870  1000  8 30 0.000 1.000 0.000 log2 -> 0.4626139682142911
  235/870  1000  8 44 0.000 1.000 0.000 auto -> 0.4592241308414393
  236/870  1000  8 44 0.000 1.000 0.000 sqrt -> 0.4695068447854564
  237/870  1000  8 44 0.000 1.000 0.000 log2 -> 0.4695068447854564
  238/870  1000  8 64 0.000 1.000 0.000 auto -> 0.46818581912238305
  239/870  1000  8 64 0.000 1.000 0.000 sqrt -> 0.47722857093086163
  240/870  1000  8 64 0.000 1.000 0.000 log2 -> 0.47722857093086163
  241/870  1000  9  2 0.000 1.000 0.000 auto -> 0.4255286131620706
  242/870  1000  9  2 0.000 1.000 0.000 sqrt -> 0.43345760786671245
  243/870  1000  9  2 0.000 1.000 0.000 log2 -> 0.43345760786671245
  244/870  1000  9  3 0.000 1.000 0.000 auto -> 0.4256822301271836
  245/870  1000  9  3 0.000 1.000 0.000 sqrt -> 0.43333076557971434
  246/870  1000  9  3 0.000 1.000 0.000 log2 -> 0.43333076557971434
  247/870  1000  9  4 0.000 1.000 0.000 auto -> 0.42625047676479905
  248/870  1000  9  4 0.000 1.000 0.000 sqrt -> 0.433905974329624
  249/870  1000  9  4 0.000 1.000 0.000 log2 -> 0.433905974329624
  250/870  1000  9  6 0.000 1.000 0.000 auto -> 0.42750416867369595
  251/870  1000  9  6 0.000 1.000 0.000 sqrt -> 0.4354554581999885
  252/870  1000  9  6 0.000 1.000 0.000 log2 -> 0.4354554581999885
  253/870  1000  9  9 0.000 1.000 0.000 auto -> 0.4298137420848127
  254/870  1000  9  9 0.000 1.000 0.000 sqrt -> 0.4386033999955601
  255/870  1000  9  9 0.000 1.000 0.000 log2 -> 0.4386033999955601
  256/870  1000  9 14 0.000 1.000 0.000 auto -> 0.43382926507715197
  257/870  1000  9 14 0.000 1.000 0.000 sqrt -> 0.44368448350673884
  258/870  1000  9 14 0.000 1.000 0.000 log2 -> 0.44368448350673884
  259/870  1000  9 20 0.000 1.000 0.000 auto -> 0.4386083148207323
  260/870  1000  9 20 0.000 1.000 0.000 sqrt -> 0.4475513700040299
  261/870  1000  9 20 0.000 1.000 0.000 log2 -> 0.4475513700040299
  262/870  1000  9 30 0.000 1.000 0.000 auto -> 0.44582708099186225
  263/870  1000  9 30 0.000 1.000 0.000 sqrt -> 0.45557656029098076
  264/870  1000  9 30 0.000 1.000 0.000 log2 -> 0.45557656029098076
  265/870  1000  9 44 0.000 1.000 0.000 auto -> 0.4545208777574154
  266/870  1000  9 44 0.000 1.000 0.000 sqrt -> 0.46305106950293956
  267/870  1000  9 44 0.000 1.000 0.000 log2 -> 0.46305106950293956
  268/870  1000  9 64 0.000 1.000 0.000 auto -> 0.4650474974042275
  269/870  1000  9 64 0.000 1.000 0.000 sqrt -> 0.4723671410907988
  270/870  1000  9 64 0.000 1.000 0.000 log2 -> 0.4723671410907988
  271/870  1000 10  2 0.000 1.000 0.000 auto -> 0.4152388217490967
  272/870  1000 10  2 0.000 1.000 0.000 sqrt -> 0.4208949461234523
  273/870  1000 10  2 0.000 1.000 0.000 log2 -> 0.4208949461234523
  274/870  1000 10  3 0.000 1.000 0.000 auto -> 0.4156685318376591
  275/870  1000 10  3 0.000 1.000 0.000 sqrt -> 0.4207729838769152
  276/870  1000 10  3 0.000 1.000 0.000 log2 -> 0.4207729838769152
  277/870  1000 10  4 0.000 1.000 0.000 auto -> 0.4164991792623752
  278/870  1000 10  4 0.000 1.000 0.000 sqrt -> 0.42239303882986295
  279/870  1000 10  4 0.000 1.000 0.000 log2 -> 0.42239303882986295
  280/870  1000 10  6 0.000 1.000 0.000 auto -> 0.4181915802710163
  281/870  1000 10  6 0.000 1.000 0.000 sqrt -> 0.425022026625395
  282/870  1000 10  6 0.000 1.000 0.000 log2 -> 0.42502202662539496
  283/870  1000 10  9 0.000 1.000 0.000 auto -> 0.42115726254819497
  284/870  1000 10  9 0.000 1.000 0.000 sqrt -> 0.42862261585425093
  285/870  1000 10  9 0.000 1.000 0.000 log2 -> 0.42862261585425093
  286/870  1000 10 14 0.000 1.000 0.000 auto -> 0.4267243235548084
  287/870  1000 10 14 0.000 1.000 0.000 sqrt -> 0.434812637481164
  288/870  1000 10 14 0.000 1.000 0.000 log2 -> 0.434812637481164
  289/870  1000 10 20 0.000 1.000 0.000 auto -> 0.4327560477926891
  290/870  1000 10 20 0.000 1.000 0.000 sqrt -> 0.44134207244325907
  291/870  1000 10 20 0.000 1.000 0.000 log2 -> 0.44134207244325907
  292/870  1000 10 30 0.000 1.000 0.000 auto -> 0.44165912171696625
  293/870  1000 10 30 0.000 1.000 0.000 sqrt -> 0.45027598751165293
  294/870  1000 10 30 0.000 1.000 0.000 log2 -> 0.45027598751165304
  295/870  1000 10 44 0.000 1.000 0.000 auto -> 0.45150231905946253
  296/870  1000 10 44 0.000 1.000 0.000 sqrt -> 0.45916744764728334
  297/870  1000 10 44 0.000 1.000 0.000 log2 -> 0.45916744764728334
  298/870  1000 10 64 0.000 1.000 0.000 auto -> 0.46313186731669015
  299/870  1000 10 64 0.000 1.000 0.000 sqrt -> 0.47027118680306307
  300/870  1000 10 64 0.000 1.000 0.000 log2 -> 0.47027118680306307
  301/870  1000 11  2 0.000 1.000 0.000 auto -> 0.40768020257822196
  302/870  1000 11  2 0.000 1.000 0.000 sqrt -> 0.4106548739405648
  303/870  1000 11  2 0.000 1.000 0.000 log2 -> 0.4106548739405649
  304/870  1000 11  3 0.000 1.000 0.000 auto -> 0.4084256112946667
  305/870  1000 11  3 0.000 1.000 0.000 sqrt -> 0.4119550945078718
  306/870  1000 11  3 0.000 1.000 0.000 log2 -> 0.4119550945078718
  307/870  1000 11  4 0.000 1.000 0.000 auto -> 0.40945441529348625
  308/870  1000 11  4 0.000 1.000 0.000 sqrt -> 0.4133022571455623
  309/870  1000 11  4 0.000 1.000 0.000 log2 -> 0.4133022571455624
  310/870  1000 11  6 0.000 1.000 0.000 auto -> 0.411409730537073
  311/870  1000 11  6 0.000 1.000 0.000 sqrt -> 0.4155556660225277
  312/870  1000 11  6 0.000 1.000 0.000 log2 -> 0.4155556660225277
  313/870  1000 11  9 0.000 1.000 0.000 auto -> 0.415193104507998
  314/870  1000 11  9 0.000 1.000 0.000 sqrt -> 0.42145180796202975
  315/870  1000 11  9 0.000 1.000 0.000 log2 -> 0.4214518079620297
  316/870  1000 11 14 0.000 1.000 0.000 auto -> 0.4216689489393315
  317/870  1000 11 14 0.000 1.000 0.000 sqrt -> 0.428104014918679
  318/870  1000 11 14 0.000 1.000 0.000 log2 -> 0.428104014918679
  319/870  1000 11 20 0.000 1.000 0.000 auto -> 0.42862641317797245
  320/870  1000 11 20 0.000 1.000 0.000 sqrt -> 0.4360311375773171
  321/870  1000 11 20 0.000 1.000 0.000 log2 -> 0.436031137577317
  322/870  1000 11 30 0.000 1.000 0.000 auto -> 0.43856668238874724
  323/870  1000 11 30 0.000 1.000 0.000 sqrt -> 0.44671918598872024
  324/870  1000 11 30 0.000 1.000 0.000 log2 -> 0.44671918598872024
  325/870  1000 11 44 0.000 1.000 0.000 auto -> 0.4496209790028433
  326/870  1000 11 44 0.000 1.000 0.000 sqrt -> 0.45641500541392505
  327/870  1000 11 44 0.000 1.000 0.000 log2 -> 0.45641500541392505
  328/870  1000 11 64 0.000 1.000 0.000 auto -> 0.4621214619549164
  329/870  1000 11 64 0.000 1.000 0.000 sqrt -> 0.46868880185562506
  330/870  1000 11 64 0.000 1.000 0.000 log2 -> 0.46868880185562506
  331/870  1000 12  2 0.000 1.000 0.000 auto -> 0.40183432803502467
  332/870  1000 12  2 0.000 1.000 0.000 sqrt -> 0.40355777894483597
  333/870  1000 12  2 0.000 1.000 0.000 log2 -> 0.4035577789448361
  334/870  1000 12  3 0.000 1.000 0.000 auto -> 0.4025332067471992
  335/870  1000 12  3 0.000 1.000 0.000 sqrt -> 0.4040209346651015
  336/870  1000 12  3 0.000 1.000 0.000 log2 -> 0.4040209346651016
  337/870  1000 12  4 0.000 1.000 0.000 auto -> 0.4038629674884308
  338/870  1000 12  4 0.000 1.000 0.000 sqrt -> 0.40630600690167656
  339/870  1000 12  4 0.000 1.000 0.000 log2 -> 0.40630600690167656
  340/870  1000 12  6 0.000 1.000 0.000 auto -> 0.4060374810768024
  341/870  1000 12  6 0.000 1.000 0.000 sqrt -> 0.4093652424196255
  342/870  1000 12  6 0.000 1.000 0.000 log2 -> 0.4093652424196255
  343/870  1000 12  9 0.000 1.000 0.000 auto -> 0.410508381680101
  344/870  1000 12  9 0.000 1.000 0.000 sqrt -> 0.4158764865887042
  345/870  1000 12  9 0.000 1.000 0.000 log2 -> 0.4158764865887042
  346/870  1000 12 14 0.000 1.000 0.000 auto -> 0.417823700514009
  347/870  1000 12 14 0.000 1.000 0.000 sqrt -> 0.42322365781503873
  348/870  1000 12 14 0.000 1.000 0.000 log2 -> 0.42322365781503873
  349/870  1000 12 20 0.000 1.000 0.000 auto -> 0.425503495639727
  350/870  1000 12 20 0.000 1.000 0.000 sqrt -> 0.43248987366264185
  351/870  1000 12 20 0.000 1.000 0.000 log2 -> 0.43248987366264185
  352/870  1000 12 30 0.000 1.000 0.000 auto -> 0.4364601428129078
  353/870  1000 12 30 0.000 1.000 0.000 sqrt -> 0.44467455406788337
  354/870  1000 12 30 0.000 1.000 0.000 log2 -> 0.44467455406788337
  355/870  1000 12 44 0.000 1.000 0.000 auto -> 0.44841188592917286
  356/870  1000 12 44 0.000 1.000 0.000 sqrt -> 0.4546229125182011
  357/870  1000 12 44 0.000 1.000 0.000 log2 -> 0.4546229125182011
  358/870  1000 12 64 0.000 1.000 0.000 auto -> 0.46138085040583476
  359/870  1000 12 64 0.000 1.000 0.000 sqrt -> 0.4677584092078539
  360/870  1000 12 64 0.000 1.000 0.000 log2 -> 0.4677584092078539
  361/870  1000 13  2 0.000 1.000 0.000 auto -> 0.39820123993566686
  362/870  1000 13  2 0.000 1.000 0.000 sqrt -> 0.3979353578374025
  363/870  1000 13  2 0.000 1.000 0.000 log2 -> 0.3979353578374025
  364/870  1000 13  3 0.000 1.000 0.000 auto -> 0.3985546195078061
  365/870  1000 13  3 0.000 1.000 0.000 sqrt -> 0.39884652583246805
  366/870  1000 13  3 0.000 1.000 0.000 log2 -> 0.39884652583246805
  367/870  1000 13  4 0.000 1.000 0.000 auto -> 0.40001014403757046
  368/870  1000 13  4 0.000 1.000 0.000 sqrt -> 0.4011246840500966
  369/870  1000 13  4 0.000 1.000 0.000 log2 -> 0.40112468405009666
  370/870  1000 13  6 0.000 1.000 0.000 auto -> 0.40262496923566166
  371/870  1000 13  6 0.000 1.000 0.000 sqrt -> 0.4048290398049624
  372/870  1000 13  6 0.000 1.000 0.000 log2 -> 0.4048290398049624
  373/870  1000 13  9 0.000 1.000 0.000 auto -> 0.40753204698362827
  374/870  1000 13  9 0.000 1.000 0.000 sqrt -> 0.4117213000283275
  375/870  1000 13  9 0.000 1.000 0.000 log2 -> 0.4117213000283275
  376/870  1000 13 14 0.000 1.000 0.000 auto -> 0.41525437614398936
  377/870  1000 13 14 0.000 1.000 0.000 sqrt -> 0.4207095111795825
  378/870  1000 13 14 0.000 1.000 0.000 log2 -> 0.4207095111795825
  379/870  1000 13 20 0.000 1.000 0.000 auto -> 0.42343870829854735
  380/870  1000 13 20 0.000 1.000 0.000 sqrt -> 0.42959746719155883
  381/870  1000 13 20 0.000 1.000 0.000 log2 -> 0.4295974671915588
  382/870  1000 13 30 0.000 1.000 0.000 auto -> 0.4350145126602707
  383/870  1000 13 30 0.000 1.000 0.000 sqrt -> 0.44236743433597764
  384/870  1000 13 30 0.000 1.000 0.000 log2 -> 0.44236743433597764
  385/870  1000 13 44 0.000 1.000 0.000 auto -> 0.44748386968456494
  386/870  1000 13 44 0.000 1.000 0.000 sqrt -> 0.4538156908219287
  387/870  1000 13 44 0.000 1.000 0.000 log2 -> 0.4538156908219287
  388/870  1000 13 64 0.000 1.000 0.000 auto -> 0.46101220110053304
  389/870  1000 13 64 0.000 1.000 0.000 sqrt -> 0.46751706582183755
  390/870  1000 13 64 0.000 1.000 0.000 log2 -> 0.46751706582183766
  391/870  1000 14  2 0.000 1.000 0.000 auto -> 0.39510369064794126
  392/870  1000 14  2 0.000 1.000 0.000 sqrt -> 0.3934743291521137
  393/870  1000 14  2 0.000 1.000 0.000 log2 -> 0.3934743291521137
  394/870  1000 14  3 0.000 1.000 0.000 auto -> 0.3957481276557419
  395/870  1000 14  3 0.000 1.000 0.000 sqrt -> 0.3959088912690704
  396/870  1000 14  3 0.000 1.000 0.000 log2 -> 0.3959088912690703
  397/870  1000 14  4 0.000 1.000 0.000 auto -> 0.39690391313367696
  398/870  1000 14  4 0.000 1.000 0.000 sqrt -> 0.3983541618710362
  399/870  1000 14  4 0.000 1.000 0.000 log2 -> 0.39835416187103617
  400/870  1000 14  6 0.000 1.000 0.000 auto -> 0.400243080206056
  401/870  1000 14  6 0.000 1.000 0.000 sqrt -> 0.40165008149300746
  402/870  1000 14  6 0.000 1.000 0.000 log2 -> 0.40165008149300735
  403/870  1000 14  9 0.000 1.000 0.000 auto -> 0.40493900725328286
  404/870  1000 14  9 0.000 1.000 0.000 sqrt -> 0.40864322630676697
  405/870  1000 14  9 0.000 1.000 0.000 log2 -> 0.40864322630676697
  406/870  1000 14 14 0.000 1.000 0.000 auto -> 0.41346433696667484
  407/870  1000 14 14 0.000 1.000 0.000 sqrt -> 0.4173236980865858
  408/870  1000 14 14 0.000 1.000 0.000 log2 -> 0.41732369808658576
  409/870  1000 14 20 0.000 1.000 0.000 auto -> 0.42194265587858837
  410/870  1000 14 20 0.000 1.000 0.000 sqrt -> 0.42832998629659996
  411/870  1000 14 20 0.000 1.000 0.000 log2 -> 0.42832998629659996
  412/870  1000 14 30 0.000 1.000 0.000 auto -> 0.43402937957781296
  413/870  1000 14 30 0.000 1.000 0.000 sqrt -> 0.44093120367629074
  414/870  1000 14 30 0.000 1.000 0.000 log2 -> 0.4409312036762907
  415/870  1000 14 44 0.000 1.000 0.000 auto -> 0.4469732941834847
  416/870  1000 14 44 0.000 1.000 0.000 sqrt -> 0.4532641851789842
  417/870  1000 14 44 0.000 1.000 0.000 log2 -> 0.4532641851789842
  418/870  1000 14 64 0.000 1.000 0.000 auto -> 0.4608130567450724
  419/870  1000 14 64 0.000 1.000 0.000 sqrt -> 0.46722363184347665
  420/870  1000 14 64 0.000 1.000 0.000 log2 -> 0.46722363184347665
  421/870  1000 15  2 0.000 1.000 0.000 auto -> 0.3930964527321457
  422/870  1000 15  2 0.000 1.000 0.000 sqrt -> 0.3919298848453381
  423/870  1000 15  2 0.000 1.000 0.000 log2 -> 0.3919298848453381
  424/870  1000 15  3 0.000 1.000 0.000 auto -> 0.3937134018196898
  425/870  1000 15  3 0.000 1.000 0.000 sqrt -> 0.39260443656593974
  426/870  1000 15  3 0.000 1.000 0.000 log2 -> 0.39260443656593974
  427/870  1000 15  4 0.000 1.000 0.000 auto -> 0.39521050660886275
  428/870  1000 15  4 0.000 1.000 0.000 sqrt -> 0.3945065725849528
  429/870  1000 15  4 0.000 1.000 0.000 log2 -> 0.39450657258495286
  430/870  1000 15  6 0.000 1.000 0.000 auto -> 0.39838055608690964
  431/870  1000 15  6 0.000 1.000 0.000 sqrt -> 0.398820734103049
  432/870  1000 15  6 0.000 1.000 0.000 log2 -> 0.39882073410304897
  433/870  1000 15  9 0.000 1.000 0.000 auto -> 0.40355359567797305
  434/870  1000 15  9 0.000 1.000 0.000 sqrt -> 0.4064003919711488
  435/870  1000 15  9 0.000 1.000 0.000 log2 -> 0.4064003919711488
  436/870  1000 15 14 0.000 1.000 0.000 auto -> 0.4119653626888764
  437/870  1000 15 14 0.000 1.000 0.000 sqrt -> 0.416905637211217
  438/870  1000 15 14 0.000 1.000 0.000 log2 -> 0.416905637211217
  439/870  1000 15 20 0.000 1.000 0.000 auto -> 0.4210968436600912
  440/870  1000 15 20 0.000 1.000 0.000 sqrt -> 0.427066516644483
  441/870  1000 15 20 0.000 1.000 0.000 log2 -> 0.427066516644483
  442/870  1000 15 30 0.000 1.000 0.000 auto -> 0.43343277281194376
  443/870  1000 15 30 0.000 1.000 0.000 sqrt -> 0.44092618755775453
  444/870  1000 15 30 0.000 1.000 0.000 log2 -> 0.44092618755775453
  445/870  1000 15 44 0.000 1.000 0.000 auto -> 0.44664525280434203
  446/870  1000 15 44 0.000 1.000 0.000 sqrt -> 0.45292896513386394
  447/870  1000 15 44 0.000 1.000 0.000 log2 -> 0.45292896513386394
  448/870  1000 15 64 0.000 1.000 0.000 auto -> 0.46071402964415226
  449/870  1000 15 64 0.000 1.000 0.000 sqrt -> 0.46705807079782297
  450/870  1000 15 64 0.000 1.000 0.000 log2 -> 0.46705807079782297
  451/870  1000 16  2 0.000 1.000 0.000 auto -> 0.3913086874382543
  452/870  1000 16  2 0.000 1.000 0.000 sqrt -> 0.38861990499105653
  453/870  1000 16  2 0.000 1.000 0.000 log2 -> 0.38861990499105653
  454/870  1000 16  3 0.000 1.000 0.000 auto -> 0.3925571902238872
  455/870  1000 16  3 0.000 1.000 0.000 sqrt -> 0.3913714683844088
  456/870  1000 16  3 0.000 1.000 0.000 log2 -> 0.3913714683844088
  457/870  1000 16  4 0.000 1.000 0.000 auto -> 0.3940701392531158
  458/870  1000 16  4 0.000 1.000 0.000 sqrt -> 0.39268075828466614
  459/870  1000 16  4 0.000 1.000 0.000 log2 -> 0.39268075828466614
  460/870  1000 16  6 0.000 1.000 0.000 auto -> 0.3975020284752258
  461/870  1000 16  6 0.000 1.000 0.000 sqrt -> 0.39770698105724994
  462/870  1000 16  6 0.000 1.000 0.000 log2 -> 0.39770698105724994
  463/870  1000 16  9 0.000 1.000 0.000 auto -> 0.4024208471499012
  464/870  1000 16  9 0.000 1.000 0.000 sqrt -> 0.4054161293481425
  465/870  1000 16  9 0.000 1.000 0.000 log2 -> 0.4054161293481425
  466/870  1000 16 14 0.000 1.000 0.000 auto -> 0.4113117173748257
  467/870  1000 16 14 0.000 1.000 0.000 sqrt -> 0.41529444605316784
  468/870  1000 16 14 0.000 1.000 0.000 log2 -> 0.41529444605316784
  469/870  1000 16 20 0.000 1.000 0.000 auto -> 0.4205506769564708
  470/870  1000 16 20 0.000 1.000 0.000 sqrt -> 0.42698037002752726
  471/870  1000 16 20 0.000 1.000 0.000 log2 -> 0.42698037002752737
  472/870  1000 16 30 0.000 1.000 0.000 auto -> 0.4331228661862119
  473/870  1000 16 30 0.000 1.000 0.000 sqrt -> 0.44044477405579524
  474/870  1000 16 30 0.000 1.000 0.000 log2 -> 0.44044477405579524
  475/870  1000 16 44 0.000 1.000 0.000 auto -> 0.4464995240139633
  476/870  1000 16 44 0.000 1.000 0.000 sqrt -> 0.45282165134122293
  477/870  1000 16 44 0.000 1.000 0.000 log2 -> 0.45282165134122293
  478/870  1000 16 64 0.000 1.000 0.000 auto -> 0.4606584180530144
  479/870  1000 16 64 0.000 1.000 0.000 sqrt -> 0.46706762264050755
  480/870  1000 16 64 0.000 1.000 0.000 log2 -> 0.46706762264050755
  481/870  1000 17  2 0.000 1.000 0.000 auto -> 0.39116034658226223
  482/870  1000 17  2 0.000 1.000 0.000 sqrt -> 0.3874746367958537
  483/870  1000 17  2 0.000 1.000 0.000 log2 -> 0.3874746367958537
  484/870  1000 17  3 0.000 1.000 0.000 auto -> 0.3920532364864758
  485/870  1000 17  3 0.000 1.000 0.000 sqrt -> 0.3896896546335637
  486/870  1000 17  3 0.000 1.000 0.000 log2 -> 0.38968965463356375
  487/870  1000 17  4 0.000 1.000 0.000 auto -> 0.39370747011852075
  488/870  1000 17  4 0.000 1.000 0.000 sqrt -> 0.3926547662683104
  489/870  1000 17  4 0.000 1.000 0.000 log2 -> 0.3926547662683104
  490/870  1000 17  6 0.000 1.000 0.000 auto -> 0.39676924257554375
  491/870  1000 17  6 0.000 1.000 0.000 sqrt -> 0.3971203706652255
  492/870  1000 17  6 0.000 1.000 0.000 log2 -> 0.3971203706652255
  493/870  1000 17  9 0.000 1.000 0.000 auto -> 0.4021951349931556
  494/870  1000 17  9 0.000 1.000 0.000 sqrt -> 0.40338572685820556
  495/870  1000 17  9 0.000 1.000 0.000 log2 -> 0.40338572685820556
  496/870  1000 17 14 0.000 1.000 0.000 auto -> 0.41094375345451334
  497/870  1000 17 14 0.000 1.000 0.000 sqrt -> 0.4148821295196692
  498/870  1000 17 14 0.000 1.000 0.000 log2 -> 0.4148821295196692
  499/870  1000 17 20 0.000 1.000 0.000 auto -> 0.420119032553075
  500/870  1000 17 20 0.000 1.000 0.000 sqrt -> 0.42665570800799096
  501/870  1000 17 20 0.000 1.000 0.000 log2 -> 0.42665570800799096
  502/870  1000 17 30 0.000 1.000 0.000 auto -> 0.4329046876736213
  503/870  1000 17 30 0.000 1.000 0.000 sqrt -> 0.4400304917368994
  504/870  1000 17 30 0.000 1.000 0.000 log2 -> 0.4400304917368994
  505/870  1000 17 44 0.000 1.000 0.000 auto -> 0.44638655739663385
  506/870  1000 17 44 0.000 1.000 0.000 sqrt -> 0.4526826013916498
  507/870  1000 17 44 0.000 1.000 0.000 log2 -> 0.4526826013916498
  508/870  1000 17 64 0.000 1.000 0.000 auto -> 0.4606319569783032
  509/870  1000 17 64 0.000 1.000 0.000 sqrt -> 0.46700284208514886
  510/870  1000 17 64 0.000 1.000 0.000 log2 -> 0.46700284208514886
  511/870  1000 18  2 0.000 1.000 0.000 auto -> 0.38997501151208924
  512/870  1000 18  2 0.000 1.000 0.000 sqrt -> 0.3868825926124692
  513/870  1000 18  2 0.000 1.000 0.000 log2 -> 0.3868825926124692
  514/870  1000 18  3 0.000 1.000 0.000 auto -> 0.3912894220470012
  515/870  1000 18  3 0.000 1.000 0.000 sqrt -> 0.3897573115882802
  516/870  1000 18  3 0.000 1.000 0.000 log2 -> 0.3897573115882802
  517/870  1000 18  4 0.000 1.000 0.000 auto -> 0.39365569829242736
  518/870  1000 18  4 0.000 1.000 0.000 sqrt -> 0.390880554872733
  519/870  1000 18  4 0.000 1.000 0.000 log2 -> 0.39088055487273293
  520/870  1000 18  6 0.000 1.000 0.000 auto -> 0.3966318950232484
  521/870  1000 18  6 0.000 1.000 0.000 sqrt -> 0.3954684885602282
  522/870  1000 18  6 0.000 1.000 0.000 log2 -> 0.39546848856022826
  523/870  1000 18  9 0.000 1.000 0.000 auto -> 0.4019993966281783
  524/870  1000 18  9 0.000 1.000 0.000 sqrt -> 0.4035057559800785
  525/870  1000 18  9 0.000 1.000 0.000 log2 -> 0.4035057559800785
  526/870  1000 18 14 0.000 1.000 0.000 auto -> 0.4106753119751945
  527/870  1000 18 14 0.000 1.000 0.000 sqrt -> 0.41456227423880243
  528/870  1000 18 14 0.000 1.000 0.000 log2 -> 0.41456227423880243
  529/870  1000 18 20 0.000 1.000 0.000 auto -> 0.41995416083899945
  530/870  1000 18 20 0.000 1.000 0.000 sqrt -> 0.4262091670385173
  531/870  1000 18 20 0.000 1.000 0.000 log2 -> 0.4262091670385173
  532/870  1000 18 30 0.000 1.000 0.000 auto -> 0.43279219941157293
  533/870  1000 18 30 0.000 1.000 0.000 sqrt -> 0.43993537429299795
  534/870  1000 18 30 0.000 1.000 0.000 log2 -> 0.43993537429299784
  535/870  1000 18 44 0.000 1.000 0.000 auto -> 0.44636973287956017
  536/870  1000 18 44 0.000 1.000 0.000 sqrt -> 0.4525895363967974
  537/870  1000 18 44 0.000 1.000 0.000 log2 -> 0.4525895363967974
  538/870  1000 18 64 0.000 1.000 0.000 auto -> 0.46062786887368784
  539/870  1000 18 64 0.000 1.000 0.000 sqrt -> 0.46696372649876944
  540/870  1000 18 64 0.000 1.000 0.000 log2 -> 0.46696372649876944
  541/870  1000 19  2 0.000 1.000 0.000 auto -> 0.3900423643092834
  542/870  1000 19  2 0.000 1.000 0.000 sqrt -> 0.38593174825931725
  543/870  1000 19  2 0.000 1.000 0.000 log2 -> 0.38593174825931725
  544/870  1000 19  3 0.000 1.000 0.000 auto -> 0.39135415490583864
  545/870  1000 19  3 0.000 1.000 0.000 sqrt -> 0.38958942001042285
  546/870  1000 19  3 0.000 1.000 0.000 log2 -> 0.3895894200104228
  547/870  1000 19  4 0.000 1.000 0.000 auto -> 0.3930688100034884
  548/870  1000 19  4 0.000 1.000 0.000 sqrt -> 0.3902680855135909
  549/870  1000 19  4 0.000 1.000 0.000 log2 -> 0.3902680855135909
  550/870  1000 19  6 0.000 1.000 0.000 auto -> 0.3964707544328568
  551/870  1000 19  6 0.000 1.000 0.000 sqrt -> 0.39530778246311354
  552/870  1000 19  6 0.000 1.000 0.000 log2 -> 0.39530778246311354
  553/870  1000 19  9 0.000 1.000 0.000 auto -> 0.40156157448076146
  554/870  1000 19  9 0.000 1.000 0.000 sqrt -> 0.403610308324132
  555/870  1000 19  9 0.000 1.000 0.000 log2 -> 0.403610308324132
  556/870  1000 19 14 0.000 1.000 0.000 auto -> 0.4104831235181348
  557/870  1000 19 14 0.000 1.000 0.000 sqrt -> 0.4144413959888067
  558/870  1000 19 14 0.000 1.000 0.000 log2 -> 0.4144413959888067
  559/870  1000 19 20 0.000 1.000 0.000 auto -> 0.41992467920842874
  560/870  1000 19 20 0.000 1.000 0.000 sqrt -> 0.426189780100706
  561/870  1000 19 20 0.000 1.000 0.000 log2 -> 0.42618978010070613
  562/870  1000 19 30 0.000 1.000 0.000 auto -> 0.4327162398596999
  563/870  1000 19 30 0.000 1.000 0.000 sqrt -> 0.4398924817861092
  564/870  1000 19 30 0.000 1.000 0.000 log2 -> 0.4398924817861092
  565/870  1000 19 44 0.000 1.000 0.000 auto -> 0.4463530027969043
  566/870  1000 19 44 0.000 1.000 0.000 sqrt -> 0.45253807502587895
  567/870  1000 19 44 0.000 1.000 0.000 log2 -> 0.45253807502587895
  568/870  1000 19 64 0.000 1.000 0.000 auto -> 0.4606112359662608
  569/870  1000 19 64 0.000 1.000 0.000 sqrt -> 0.4669710517450624
  570/870  1000 19 64 0.000 1.000 0.000 log2 -> 0.4669710517450624
  571/870  1000 20  2 0.000 1.000 0.000 auto -> 0.3897587709874692
  572/870  1000 20  2 0.000 1.000 0.000 sqrt -> 0.385514919205521
  573/870  1000 20  2 0.000 1.000 0.000 log2 -> 0.385514919205521
  574/870  1000 20  3 0.000 1.000 0.000 auto -> 0.3913309507240205
  575/870  1000 20  3 0.000 1.000 0.000 sqrt -> 0.3882047761550537
  576/870  1000 20  3 0.000 1.000 0.000 log2 -> 0.3882047761550537
  577/870  1000 20  4 0.000 1.000 0.000 auto -> 0.3928198007198609
  578/870  1000 20  4 0.000 1.000 0.000 sqrt -> 0.3911101376530192
  579/870  1000 20  4 0.000 1.000 0.000 log2 -> 0.3911101376530192
  580/870  1000 20  6 0.000 1.000 0.000 auto -> 0.39632987354599686
  581/870  1000 20  6 0.000 1.000 0.000 sqrt -> 0.39487029700230947
  582/870  1000 20  6 0.000 1.000 0.000 log2 -> 0.39487029700230947
  583/870  1000 20  9 0.000 1.000 0.000 auto -> 0.40140629574697356
  584/870  1000 20  9 0.000 1.000 0.000 sqrt -> 0.4037487318686761
  585/870  1000 20  9 0.000 1.000 0.000 log2 -> 0.4037487318686761
  586/870  1000 20 14 0.000 1.000 0.000 auto -> 0.4103586163813509
  587/870  1000 20 14 0.000 1.000 0.000 sqrt -> 0.41444489538018764
  588/870  1000 20 14 0.000 1.000 0.000 log2 -> 0.41444489538018764
  589/870  1000 20 20 0.000 1.000 0.000 auto -> 0.41981040039740974
  590/870  1000 20 20 0.000 1.000 0.000 sqrt -> 0.4259516601690337
  591/870  1000 20 20 0.000 1.000 0.000 log2 -> 0.42595166016903363
  592/870  1000 20 30 0.000 1.000 0.000 auto -> 0.432637161960751
  593/870  1000 20 30 0.000 1.000 0.000 sqrt -> 0.43973315026372856
  594/870  1000 20 30 0.000 1.000 0.000 log2 -> 0.43973315026372856
  595/870  1000 20 44 0.000 1.000 0.000 auto -> 0.4463309173881609
  596/870  1000 20 44 0.000 1.000 0.000 sqrt -> 0.4525164190199344
  597/870  1000 20 44 0.000 1.000 0.000 log2 -> 0.4525164190199344
  598/870  1000 20 64 0.000 1.000 0.000 auto -> 0.4606137395342445
  599/870  1000 20 64 0.000 1.000 0.000 sqrt -> 0.46697022178673103
  600/870  1000 20 64 0.000 1.000 0.000 log2 -> 0.46697022178673103
  601/870  1000 21  2 0.000 1.000 0.000 auto -> 0.38994714981806006
  602/870  1000 21  2 0.000 1.000 0.000 sqrt -> 0.38578981509071303
  603/870  1000 21  2 0.000 1.000 0.000 log2 -> 0.38578981509071303
  604/870  1000 21  3 0.000 1.000 0.000 auto -> 0.39109818994163364
  605/870  1000 21  3 0.000 1.000 0.000 sqrt -> 0.38871074793829613
  606/870  1000 21  3 0.000 1.000 0.000 log2 -> 0.38871074793829613
  607/870  1000 21  4 0.000 1.000 0.000 auto -> 0.3925264804218711
  608/870  1000 21  4 0.000 1.000 0.000 sqrt -> 0.39127586946255627
  609/870  1000 21  4 0.000 1.000 0.000 log2 -> 0.39127586946255627
  610/870  1000 21  6 0.000 1.000 0.000 auto -> 0.3961474109529524
  611/870  1000 21  6 0.000 1.000 0.000 sqrt -> 0.39507082244507297
  612/870  1000 21  6 0.000 1.000 0.000 log2 -> 0.39507082244507297
  613/870  1000 21  9 0.000 1.000 0.000 auto -> 0.40145262366499274
  614/870  1000 21  9 0.000 1.000 0.000 sqrt -> 0.4038913921300878
  615/870  1000 21  9 0.000 1.000 0.000 log2 -> 0.4038913921300879
  616/870  1000 21 14 0.000 1.000 0.000 auto -> 0.41029181080146476
  617/870  1000 21 14 0.000 1.000 0.000 sqrt -> 0.41423809096339737
  618/870  1000 21 14 0.000 1.000 0.000 log2 -> 0.41423809096339737
  619/870  1000 21 20 0.000 1.000 0.000 auto -> 0.4197894182080024
  620/870  1000 21 20 0.000 1.000 0.000 sqrt -> 0.4262015009211102
  621/870  1000 21 20 0.000 1.000 0.000 log2 -> 0.4262015009211102
  622/870  1000 21 30 0.000 1.000 0.000 auto -> 0.4326293197463076
  623/870  1000 21 30 0.000 1.000 0.000 sqrt -> 0.4396969837371896
  624/870  1000 21 30 0.000 1.000 0.000 log2 -> 0.4396969837371895
  625/870  1000 21 44 0.000 1.000 0.000 auto -> 0.44633063953654034
  626/870  1000 21 44 0.000 1.000 0.000 sqrt -> 0.4524702264945953
  627/870  1000 21 44 0.000 1.000 0.000 log2 -> 0.4524702264945953
  628/870  1000 21 64 0.000 1.000 0.000 auto -> 0.46061421474308656
  629/870  1000 21 64 0.000 1.000 0.000 sqrt -> 0.4669855194373558
  630/870  1000 21 64 0.000 1.000 0.000 log2 -> 0.4669855194373558
  631/870  1000 22  2 0.000 1.000 0.000 auto -> 0.38996881068338435
  632/870  1000 22  2 0.000 1.000 0.000 sqrt -> 0.38471068976628625
  633/870  1000 22  2 0.000 1.000 0.000 log2 -> 0.38471068976628625
  634/870  1000 22  3 0.000 1.000 0.000 auto -> 0.3913792293230567
  635/870  1000 22  3 0.000 1.000 0.000 sqrt -> 0.3889082241808812
  636/870  1000 22  3 0.000 1.000 0.000 log2 -> 0.38890822418088117
  637/870  1000 22  4 0.000 1.000 0.000 auto -> 0.3925545450833039
  638/870  1000 22  4 0.000 1.000 0.000 sqrt -> 0.3905967899199562
  639/870  1000 22  4 0.000 1.000 0.000 log2 -> 0.3905967899199562
  640/870  1000 22  6 0.000 1.000 0.000 auto -> 0.3960610183285384
  641/870  1000 22  6 0.000 1.000 0.000 sqrt -> 0.3948227490901591
  642/870  1000 22  6 0.000 1.000 0.000 log2 -> 0.3948227490901591
  643/870  1000 22  9 0.000 1.000 0.000 auto -> 0.4013933881600537
  644/870  1000 22  9 0.000 1.000 0.000 sqrt -> 0.403685498093064
  645/870  1000 22  9 0.000 1.000 0.000 log2 -> 0.403685498093064
  646/870  1000 22 14 0.000 1.000 0.000 auto -> 0.4102880519373029
  647/870  1000 22 14 0.000 1.000 0.000 sqrt -> 0.41435411292999663
  648/870  1000 22 14 0.000 1.000 0.000 log2 -> 0.41435411292999663
  649/870  1000 22 20 0.000 1.000 0.000 auto -> 0.41977270745142786
  650/870  1000 22 20 0.000 1.000 0.000 sqrt -> 0.4262967086446378
  651/870  1000 22 20 0.000 1.000 0.000 log2 -> 0.42629670864463787
  652/870  1000 22 30 0.000 1.000 0.000 auto -> 0.432623085048089
  653/870  1000 22 30 0.000 1.000 0.000 sqrt -> 0.4397356703137847
  654/870  1000 22 30 0.000 1.000 0.000 log2 -> 0.43973567031378463
  655/870  1000 22 44 0.000 1.000 0.000 auto -> 0.44632367786177896
  656/870  1000 22 44 0.000 1.000 0.000 sqrt -> 0.45251188748617494
  657/870  1000 22 44 0.000 1.000 0.000 log2 -> 0.45251188748617494
  658/870  1000 22 64 0.000 1.000 0.000 auto -> 0.4606170800650649
  659/870  1000 22 64 0.000 1.000 0.000 sqrt -> 0.46695782394098373
  660/870  1000 22 64 0.000 1.000 0.000 log2 -> 0.46695782394098373
  661/870  1000 23  2 0.000 1.000 0.000 auto -> 0.3898158479317166
  662/870  1000 23  2 0.000 1.000 0.000 sqrt -> 0.3843721213738536
  663/870  1000 23  2 0.000 1.000 0.000 log2 -> 0.3843721213738536
  664/870  1000 23  3 0.000 1.000 0.000 auto -> 0.39130257396340484
  665/870  1000 23  3 0.000 1.000 0.000 sqrt -> 0.3889842917118757
  666/870  1000 23  3 0.000 1.000 0.000 log2 -> 0.3889842917118757
  667/870  1000 23  4 0.000 1.000 0.000 auto -> 0.3924742670788248
  668/870  1000 23  4 0.000 1.000 0.000 sqrt -> 0.39076489611195714
  669/870  1000 23  4 0.000 1.000 0.000 log2 -> 0.39076489611195714
  670/870  1000 23  6 0.000 1.000 0.000 auto -> 0.3959587092180478
  671/870  1000 23  6 0.000 1.000 0.000 sqrt -> 0.3950434735997807
  672/870  1000 23  6 0.000 1.000 0.000 log2 -> 0.3950434735997807
  673/870  1000 23  9 0.000 1.000 0.000 auto -> 0.4014057429944465
  674/870  1000 23  9 0.000 1.000 0.000 sqrt -> 0.40353829448549466
  675/870  1000 23  9 0.000 1.000 0.000 log2 -> 0.40353829448549466
  676/870  1000 23 14 0.000 1.000 0.000 auto -> 0.4103011893273197
  677/870  1000 23 14 0.000 1.000 0.000 sqrt -> 0.41432498260374534
  678/870  1000 23 14 0.000 1.000 0.000 log2 -> 0.4143249826037454
  679/870  1000 23 20 0.000 1.000 0.000 auto -> 0.4197466751080612
  680/870  1000 23 20 0.000 1.000 0.000 sqrt -> 0.42618872943565655
  681/870  1000 23 20 0.000 1.000 0.000 log2 -> 0.4261887294356566
  682/870  1000 23 30 0.000 1.000 0.000 auto -> 0.4326252178226005
  683/870  1000 23 30 0.000 1.000 0.000 sqrt -> 0.43971026305141553
  684/870  1000 23 30 0.000 1.000 0.000 log2 -> 0.43971026305141553
  685/870  1000 23 44 0.000 1.000 0.000 auto -> 0.44631959262401616
  686/870  1000 23 44 0.000 1.000 0.000 sqrt -> 0.452506520356785
  687/870  1000 23 44 0.000 1.000 0.000 log2 -> 0.452506520356785
  688/870  1000 23 64 0.000 1.000 0.000 auto -> 0.46061722823965034
  689/870  1000 23 64 0.000 1.000 0.000 sqrt -> 0.4669430936662897
  690/870  1000 23 64 0.000 1.000 0.000 log2 -> 0.4669430936662897
  691/870  1000 24  2 0.000 1.000 0.000 auto -> 0.38974854247088286
  692/870  1000 24  2 0.000 1.000 0.000 sqrt -> 0.3850558534848127
  693/870  1000 24  2 0.000 1.000 0.000 log2 -> 0.3850558534848127
  694/870  1000 24  3 0.000 1.000 0.000 auto -> 0.39122804681990625
  695/870  1000 24  3 0.000 1.000 0.000 sqrt -> 0.3892394143159184
  696/870  1000 24  3 0.000 1.000 0.000 log2 -> 0.3892394143159184
  697/870  1000 24  4 0.000 1.000 0.000 auto -> 0.39251727402121483
  698/870  1000 24  4 0.000 1.000 0.000 sqrt -> 0.39057040513181934
  699/870  1000 24  4 0.000 1.000 0.000 log2 -> 0.39057040513181934
  700/870  1000 24  6 0.000 1.000 0.000 auto -> 0.39601221411706716
  701/870  1000 24  6 0.000 1.000 0.000 sqrt -> 0.3951632782862437
  702/870  1000 24  6 0.000 1.000 0.000 log2 -> 0.3951632782862437
  703/870  1000 24  9 0.000 1.000 0.000 auto -> 0.40141999028712316
  704/870  1000 24  9 0.000 1.000 0.000 sqrt -> 0.4036710759275132
  705/870  1000 24  9 0.000 1.000 0.000 log2 -> 0.4036710759275132
  706/870  1000 24 14 0.000 1.000 0.000 auto -> 0.4102827581066655
  707/870  1000 24 14 0.000 1.000 0.000 sqrt -> 0.4142615449224131
  708/870  1000 24 14 0.000 1.000 0.000 log2 -> 0.4142615449224132
  709/870  1000 24 20 0.000 1.000 0.000 auto -> 0.4197574171473639
  710/870  1000 24 20 0.000 1.000 0.000 sqrt -> 0.42613882975621875
  711/870  1000 24 20 0.000 1.000 0.000 log2 -> 0.42613882975621875
  712/870  1000 24 30 0.000 1.000 0.000 auto -> 0.43262250564565624
  713/870  1000 24 30 0.000 1.000 0.000 sqrt -> 0.4397553520889326
  714/870  1000 24 30 0.000 1.000 0.000 log2 -> 0.4397553520889326
  715/870  1000 24 44 0.000 1.000 0.000 auto -> 0.44632283035598275
  716/870  1000 24 44 0.000 1.000 0.000 sqrt -> 0.4524886752978889
  717/870  1000 24 44 0.000 1.000 0.000 log2 -> 0.4524886752978887
  718/870  1000 24 64 0.000 1.000 0.000 auto -> 0.46061721761673063
  719/870  1000 24 64 0.000 1.000 0.000 sqrt -> 0.46694267536355555
  720/870  1000 24 64 0.000 1.000 0.000 log2 -> 0.4669426753635555
  721/870  1000 25  2 0.000 1.000 0.000 auto -> 0.38968425022605907
  722/870  1000 25  2 0.000 1.000 0.000 sqrt -> 0.3850115875244529
  723/870  1000 25  2 0.000 1.000 0.000 log2 -> 0.3850115875244529
  724/870  1000 25  3 0.000 1.000 0.000 auto -> 0.3910948520505328
  725/870  1000 25  3 0.000 1.000 0.000 sqrt -> 0.3891794709579009
  726/870  1000 25  3 0.000 1.000 0.000 log2 -> 0.3891794709579009
  727/870  1000 25  4 0.000 1.000 0.000 auto -> 0.3925625098138548
  728/870  1000 25  4 0.000 1.000 0.000 sqrt -> 0.39045491363897544
  729/870  1000 25  4 0.000 1.000 0.000 log2 -> 0.3904549136389754
  730/870  1000 25  6 0.000 1.000 0.000 auto -> 0.396029860871788
  731/870  1000 25  6 0.000 1.000 0.000 sqrt -> 0.3950810358359464
  732/870  1000 25  6 0.000 1.000 0.000 log2 -> 0.3950810358359464
  733/870  1000 25  9 0.000 1.000 0.000 auto -> 0.4014056462608238
  734/870  1000 25  9 0.000 1.000 0.000 sqrt -> 0.40361915330352893
  735/870  1000 25  9 0.000 1.000 0.000 log2 -> 0.40361915330352893
  736/870  1000 25 14 0.000 1.000 0.000 auto -> 0.41027783967354264
  737/870  1000 25 14 0.000 1.000 0.000 sqrt -> 0.414205380772142
  738/870  1000 25 14 0.000 1.000 0.000 log2 -> 0.414205380772142
  739/870  1000 25 20 0.000 1.000 0.000 auto -> 0.4197731822580802
  740/870  1000 25 20 0.000 1.000 0.000 sqrt -> 0.42615044921954587
  741/870  1000 25 20 0.000 1.000 0.000 log2 -> 0.42615044921954587
  742/870  1000 25 30 0.000 1.000 0.000 auto -> 0.4326229064537255
  743/870  1000 25 30 0.000 1.000 0.000 sqrt -> 0.43972253467001504
  744/870  1000 25 30 0.000 1.000 0.000 log2 -> 0.43972253467001504
  745/870  1000 25 44 0.000 1.000 0.000 auto -> 0.4463219566964705
  746/870  1000 25 44 0.000 1.000 0.000 sqrt -> 0.4524919238511911
  747/870  1000 25 44 0.000 1.000 0.000 log2 -> 0.4524919238511911
  748/870  1000 25 64 0.000 1.000 0.000 auto -> 0.4606186652991498
  749/870  1000 25 64 0.000 1.000 0.000 sqrt -> 0.4669534442750258
  750/870  1000 25 64 0.000 1.000 0.000 log2 -> 0.4669534442750258
  751/870  1000 26  2 0.000 1.000 0.000 auto -> 0.3897062470289309
  752/870  1000 26  2 0.000 1.000 0.000 sqrt -> 0.3851106871444907
  753/870  1000 26  2 0.000 1.000 0.000 log2 -> 0.3851106871444907
  754/870  1000 26  3 0.000 1.000 0.000 auto -> 0.39106598565370426
  755/870  1000 26  3 0.000 1.000 0.000 sqrt -> 0.3891070864636961
  756/870  1000 26  3 0.000 1.000 0.000 log2 -> 0.3891070864636961
  757/870  1000 26  4 0.000 1.000 0.000 auto -> 0.39253910329151404
  758/870  1000 26  4 0.000 1.000 0.000 sqrt -> 0.39044909044385195
  759/870  1000 26  4 0.000 1.000 0.000 log2 -> 0.39044909044385195
  760/870  1000 26  6 0.000 1.000 0.000 auto -> 0.3960242767814833
  761/870  1000 26  6 0.000 1.000 0.000 sqrt -> 0.3951368784728062
  762/870  1000 26  6 0.000 1.000 0.000 log2 -> 0.3951368784728062
  763/870  1000 26  9 0.000 1.000 0.000 auto -> 0.40140337581650415
  764/870  1000 26  9 0.000 1.000 0.000 sqrt -> 0.4036105560231418
  765/870  1000 26  9 0.000 1.000 0.000 log2 -> 0.4036105560231418
  766/870  1000 26 14 0.000 1.000 0.000 auto -> 0.41027916023814054
  767/870  1000 26 14 0.000 1.000 0.000 sqrt -> 0.41416186251401116
  768/870  1000 26 14 0.000 1.000 0.000 log2 -> 0.4141618625140111
  769/870  1000 26 20 0.000 1.000 0.000 auto -> 0.41977053794682456
  770/870  1000 26 20 0.000 1.000 0.000 sqrt -> 0.42617048328123586
  771/870  1000 26 20 0.000 1.000 0.000 log2 -> 0.4261704832812358
  772/870  1000 26 30 0.000 1.000 0.000 auto -> 0.4326236358434836
  773/870  1000 26 30 0.000 1.000 0.000 sqrt -> 0.43970234189400076
  774/870  1000 26 30 0.000 1.000 0.000 log2 -> 0.43970234189400087
  775/870  1000 26 44 0.000 1.000 0.000 auto -> 0.44632408107449106
  776/870  1000 26 44 0.000 1.000 0.000 sqrt -> 0.4524911229847907
  777/870  1000 26 44 0.000 1.000 0.000 log2 -> 0.4524911229847906
  778/870  1000 26 64 0.000 1.000 0.000 auto -> 0.46061982982202926
  779/870  1000 26 64 0.000 1.000 0.000 sqrt -> 0.46695187800449256
  780/870  1000 26 64 0.000 1.000 0.000 log2 -> 0.46695187800449256
  781/870  1000 27  2 0.000 1.000 0.000 auto -> 0.3896424911332597
  782/870  1000 27  2 0.000 1.000 0.000 sqrt -> 0.38505253732799055
  783/870  1000 27  2 0.000 1.000 0.000 log2 -> 0.38505253732799055
  784/870  1000 27  3 0.000 1.000 0.000 auto -> 0.39108070590121596
  785/870  1000 27  3 0.000 1.000 0.000 sqrt -> 0.389242038929196
  786/870  1000 27  3 0.000 1.000 0.000 log2 -> 0.389242038929196
  787/870  1000 27  4 0.000 1.000 0.000 auto -> 0.3925592407925665
  788/870  1000 27  4 0.000 1.000 0.000 sqrt -> 0.39043200581025445
  789/870  1000 27  4 0.000 1.000 0.000 log2 -> 0.39043200581025445
  790/870  1000 27  6 0.000 1.000 0.000 auto -> 0.3960255514386031
  791/870  1000 27  6 0.000 1.000 0.000 sqrt -> 0.39514446139855225
  792/870  1000 27  6 0.000 1.000 0.000 log2 -> 0.39514446139855225
  793/870  1000 27  9 0.000 1.000 0.000 auto -> 0.4014065642844778
  794/870  1000 27  9 0.000 1.000 0.000 sqrt -> 0.40359989729454887
  795/870  1000 27  9 0.000 1.000 0.000 log2 -> 0.40359989729454876
  796/870  1000 27 14 0.000 1.000 0.000 auto -> 0.41027107264187723
  797/870  1000 27 14 0.000 1.000 0.000 sqrt -> 0.4141458536853008
  798/870  1000 27 14 0.000 1.000 0.000 log2 -> 0.4141458536853008
  799/870  1000 27 20 0.000 1.000 0.000 auto -> 0.41976529505078664
  800/870  1000 27 20 0.000 1.000 0.000 sqrt -> 0.42618492272807235
  801/870  1000 27 20 0.000 1.000 0.000 log2 -> 0.42618492272807235
  802/870  1000 27 30 0.000 1.000 0.000 auto -> 0.43262296671095424
  803/870  1000 27 30 0.000 1.000 0.000 sqrt -> 0.43970182283817333
  804/870  1000 27 30 0.000 1.000 0.000 log2 -> 0.43970182283817333
  805/870  1000 27 44 0.000 1.000 0.000 auto -> 0.446324108513088
  806/870  1000 27 44 0.000 1.000 0.000 sqrt -> 0.4524911229847906
  807/870  1000 27 44 0.000 1.000 0.000 log2 -> 0.4524911229847906
  808/870  1000 27 64 0.000 1.000 0.000 auto -> 0.46061982982202926
  809/870  1000 27 64 0.000 1.000 0.000 sqrt -> 0.46695187800449256
  810/870  1000 27 64 0.000 1.000 0.000 log2 -> 0.46695187800449256
  811/870  1000 28  2 0.000 1.000 0.000 auto -> 0.3896637705538974
  812/870  1000 28  2 0.000 1.000 0.000 sqrt -> 0.38505356367723403
  813/870  1000 28  2 0.000 1.000 0.000 log2 -> 0.38505356367723403
  814/870  1000 28  3 0.000 1.000 0.000 auto -> 0.3910827696920453
  815/870  1000 28  3 0.000 1.000 0.000 sqrt -> 0.38912941057363165
  816/870  1000 28  3 0.000 1.000 0.000 log2 -> 0.38912941057363165
  817/870  1000 28  4 0.000 1.000 0.000 auto -> 0.39254981807784034
  818/870  1000 28  4 0.000 1.000 0.000 sqrt -> 0.390493791297924
  819/870  1000 28  4 0.000 1.000 0.000 log2 -> 0.3904937912979239
  820/870  1000 28  6 0.000 1.000 0.000 auto -> 0.396022094669372
  821/870  1000 28  6 0.000 1.000 0.000 sqrt -> 0.3951726750127878
  822/870  1000 28  6 0.000 1.000 0.000 log2 -> 0.3951726750127878
  823/870  1000 28  9 0.000 1.000 0.000 auto -> 0.401401150492245
  824/870  1000 28  9 0.000 1.000 0.000 sqrt -> 0.40361745999733883
  825/870  1000 28  9 0.000 1.000 0.000 log2 -> 0.40361745999733895
  826/870  1000 28 14 0.000 1.000 0.000 auto -> 0.41027776351316186
  827/870  1000 28 14 0.000 1.000 0.000 sqrt -> 0.41416317093572075
  828/870  1000 28 14 0.000 1.000 0.000 log2 -> 0.41416317093572075
  829/870  1000 28 20 0.000 1.000 0.000 auto -> 0.4197648115268066
  830/870  1000 28 20 0.000 1.000 0.000 sqrt -> 0.42616194007052005
  831/870  1000 28 20 0.000 1.000 0.000 log2 -> 0.4261619400705201
  832/870  1000 28 30 0.000 1.000 0.000 auto -> 0.43262243384597726
  833/870  1000 28 30 0.000 1.000 0.000 sqrt -> 0.43970182283817333
  834/870  1000 28 30 0.000 1.000 0.000 log2 -> 0.43970182283817333
  835/870  1000 28 44 0.000 1.000 0.000 auto -> 0.44632375792489154
  836/870  1000 28 44 0.000 1.000 0.000 sqrt -> 0.4524911229847906
  837/870  1000 28 44 0.000 1.000 0.000 log2 -> 0.4524911229847906
  838/870  1000 28 64 0.000 1.000 0.000 auto -> 0.46061982982202926
  839/870  1000 28 64 0.000 1.000 0.000 sqrt -> 0.46695187800449256
  840/870  1000 28 64 0.000 1.000 0.000 log2 -> 0.4669518780044925
  841/870  1000 29  2 0.000 1.000 0.000 auto -> 0.3896728060696774
  842/870  1000 29  2 0.000 1.000 0.000 sqrt -> 0.38500080900839584
  843/870  1000 29  2 0.000 1.000 0.000 log2 -> 0.38500080900839584
  844/870  1000 29  3 0.000 1.000 0.000 auto -> 0.3910962025750861
  845/870  1000 29  3 0.000 1.000 0.000 sqrt -> 0.3891562040133909
  846/870  1000 29  3 0.000 1.000 0.000 log2 -> 0.3891562040133909
  847/870  1000 29  4 0.000 1.000 0.000 auto -> 0.39255167928272666
  848/870  1000 29  4 0.000 1.000 0.000 sqrt -> 0.39050146879691816
  849/870  1000 29  4 0.000 1.000 0.000 log2 -> 0.39050146879691816
  850/870  1000 29  6 0.000 1.000 0.000 auto -> 0.39602534828250086
  851/870  1000 29  6 0.000 1.000 0.000 sqrt -> 0.3951264819395808
  852/870  1000 29  6 0.000 1.000 0.000 log2 -> 0.39512648193958083
  853/870  1000 29  9 0.000 1.000 0.000 auto -> 0.40140279079667623
  854/870  1000 29  9 0.000 1.000 0.000 sqrt -> 0.4036041303660367
  855/870  1000 29  9 0.000 1.000 0.000 log2 -> 0.40360413036603665
  856/870  1000 29 14 0.000 1.000 0.000 auto -> 0.41027080485248774
  857/870  1000 29 14 0.000 1.000 0.000 sqrt -> 0.4141720207650137
  858/870  1000 29 14 0.000 1.000 0.000 log2 -> 0.4141720207650137
  859/870  1000 29 20 0.000 1.000 0.000 auto -> 0.4197605101162413
  860/870  1000 29 20 0.000 1.000 0.000 sqrt -> 0.42617361421141275
  861/870  1000 29 20 0.000 1.000 0.000 log2 -> 0.42617361421141275
  862/870  1000 29 30 0.000 1.000 0.000 auto -> 0.4326205413592327
  863/870  1000 29 30 0.000 1.000 0.000 sqrt -> 0.43970182283817333
  864/870  1000 29 30 0.000 1.000 0.000 log2 -> 0.43970182283817333
  865/870  1000 29 44 0.000 1.000 0.000 auto -> 0.4463234986223216
  866/870  1000 29 44 0.000 1.000 0.000 sqrt -> 0.4524911229847907
  867/870  1000 29 44 0.000 1.000 0.000 log2 -> 0.4524911229847907
  868/870  1000 29 64 0.000 1.000 0.000 auto -> 0.46061982982202926
  869/870  1000 29 64 0.000 1.000 0.000 sqrt -> 0.46695187800449256
  870/870  1000 29 64 0.000 1.000 0.000 log2 -> 0.46695187800449256
Time Taken 5141.224s

Hyperparametersuche SVR mit Feature Selection

train_df, test_df = get_datasets(logarithm=True, feature_selection=True)

X_train = train_df.drop("quality",axis=1).values
X_test = test_df.drop("quality",axis=1).values

y_train = train_df["quality"].values
y_test = test_df["quality"].values

# Avaliable Kernels are {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’}
train_config = SupportVectorRegressor_train(X_train,y_train,cross_validation=True, kernel = "rbf", test_for_professor = test_for_professor)

df = pd.DataFrame.from_dict(train_config)
df = df.sort_values(by=['mse_val'])
if not test_for_professor:
    df.to_csv('results/train_conf_svr_feat_sel.csv',index=False)

    1/3780 0.050 0.032 scale StandardScaler() 0 -> 0.5787432849079324
    2/3780 0.050 0.032 scale MinMaxScaler()   0 -> 0.5749910230560764
    3/3780 0.050 0.032 auto StandardScaler() 0 -> 0.5787432849079449
    4/3780 0.050 0.032 auto MinMaxScaler()   0 -> 0.6812117397796916
    5/3780 0.050 0.032 0.01 StandardScaler() 0 -> 0.676191344770916
    6/3780 0.050 0.032 0.01 MinMaxScaler()   0 -> 0.7451503778890992
    7/3780 0.050 0.032 0.03162277660168379 StandardScaler() 0 -> 0.6160805976136176
    8/3780 0.050 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7265186614329805
    9/3780 0.050 0.032 0.1 StandardScaler() 0 -> 0.5740999404684444
   10/3780 0.050 0.032 0.1 MinMaxScaler()   0 -> 0.7024556054607523
   11/3780 0.050 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5895977917688922
   12/3780 0.050 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6611517368919119
   13/3780 0.050 0.032 1.0 StandardScaler() 0 -> 0.6473299063870331
   14/3780 0.050 0.032 1.0 MinMaxScaler()   0 -> 0.5992666534925047
   15/3780 0.050 0.040 scale StandardScaler() 0 -> 0.571966251585832
   16/3780 0.050 0.040 scale MinMaxScaler()   0 -> 0.5679980203102948
   17/3780 0.050 0.040 auto StandardScaler() 0 -> 0.5719662515857289
   18/3780 0.050 0.040 auto MinMaxScaler()   0 -> 0.6703956220233583
   19/3780 0.050 0.040 0.01 StandardScaler() 0 -> 0.6646308144296129
   20/3780 0.050 0.040 0.01 MinMaxScaler()   0 -> 0.7420135282011607
   21/3780 0.050 0.040 0.03162277660168379 StandardScaler() 0 -> 0.6002143236411074
   22/3780 0.050 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7226783831248853
   23/3780 0.050 0.040 0.1 StandardScaler() 0 -> 0.5663749651215254
   24/3780 0.050 0.040 0.1 MinMaxScaler()   0 -> 0.6956280873830712
   25/3780 0.050 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5820995379833214
   26/3780 0.050 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.6484738278302593
   27/3780 0.050 0.040 1.0 StandardScaler() 0 -> 0.6330299090791504
   28/3780 0.050 0.040 1.0 MinMaxScaler()   0 -> 0.5860430577643934
   29/3780 0.050 0.051 scale StandardScaler() 0 -> 0.566258753239996
   30/3780 0.050 0.051 scale MinMaxScaler()   0 -> 0.56342094508611
   31/3780 0.050 0.051 auto StandardScaler() 0 -> 0.5662587532400573
   32/3780 0.050 0.051 auto MinMaxScaler()   0 -> 0.6585539713009593
   33/3780 0.050 0.051 0.01 StandardScaler() 0 -> 0.6517050079724837
   34/3780 0.050 0.051 0.01 MinMaxScaler()   0 -> 0.7381325656707859
   35/3780 0.050 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5870090805731348
   36/3780 0.050 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7178263436963133
   37/3780 0.050 0.051 0.1 StandardScaler() 0 -> 0.5602906546656558
   38/3780 0.050 0.051 0.1 MinMaxScaler()   0 -> 0.6878804580075172
   39/3780 0.050 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5753810911135203
   40/3780 0.050 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.6338511418340568
   41/3780 0.050 0.051 1.0 StandardScaler() 0 -> 0.6198472599748984
   42/3780 0.050 0.051 1.0 MinMaxScaler()   0 -> 0.576710714781291
   43/3780 0.050 0.065 scale StandardScaler() 0 -> 0.5628082895265043
   44/3780 0.050 0.065 scale MinMaxScaler()   0 -> 0.5601295490004297
   45/3780 0.050 0.065 auto StandardScaler() 0 -> 0.5628082895264231
   46/3780 0.050 0.065 auto MinMaxScaler()   0 -> 0.6455246598422542
   47/3780 0.050 0.065 0.01 StandardScaler() 0 -> 0.6376819772817987
   48/3780 0.050 0.065 0.01 MinMaxScaler()   0 -> 0.7335916905538656
   49/3780 0.050 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5773489723405104
   50/3780 0.050 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.7128036110439423
   51/3780 0.050 0.065 0.1 StandardScaler() 0 -> 0.5562573295529454
   52/3780 0.050 0.065 0.1 MinMaxScaler()   0 -> 0.6788240620608724
   53/3780 0.050 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5702403350648106
   54/3780 0.050 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.6188222424112995
   55/3780 0.050 0.065 1.0 StandardScaler() 0 -> 0.607843945793024
   56/3780 0.050 0.065 1.0 MinMaxScaler()   0 -> 0.5691138602831788
   57/3780 0.050 0.082 scale StandardScaler() 0 -> 0.5601799980747262
   58/3780 0.050 0.082 scale MinMaxScaler()   0 -> 0.5582003117315438
   59/3780 0.050 0.082 auto StandardScaler() 0 -> 0.5601799980747808
   60/3780 0.050 0.082 auto MinMaxScaler()   0 -> 0.6309446028170801
   61/3780 0.050 0.082 0.01 StandardScaler() 0 -> 0.6227818563686952
   62/3780 0.050 0.082 0.01 MinMaxScaler()   0 -> 0.7294684814161724
   63/3780 0.050 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5702667852234563
   64/3780 0.050 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.707220749376499
   65/3780 0.050 0.082 0.1 StandardScaler() 0 -> 0.5537191724106026
   66/3780 0.050 0.082 0.1 MinMaxScaler()   0 -> 0.6680744677458662
   67/3780 0.050 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5661683372543688
   68/3780 0.050 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.6036301651968506
   69/3780 0.050 0.082 1.0 StandardScaler() 0 -> 0.596374723476225
   70/3780 0.050 0.082 1.0 MinMaxScaler()   0 -> 0.5615331460755084
   71/3780 0.050 0.104 scale StandardScaler() 0 -> 0.5580070287613634
   72/3780 0.050 0.104 scale MinMaxScaler()   0 -> 0.5564515069843843
   73/3780 0.050 0.104 auto StandardScaler() 0 -> 0.5580070287614373
   74/3780 0.050 0.104 auto MinMaxScaler()   0 -> 0.6161654249564088
   75/3780 0.050 0.104 0.01 StandardScaler() 0 -> 0.6080316093192845
   76/3780 0.050 0.104 0.01 MinMaxScaler()   0 -> 0.7256884297395727
   77/3780 0.050 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5630460217748819
   78/3780 0.050 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.7008214745005944
   79/3780 0.050 0.104 0.1 StandardScaler() 0 -> 0.5526785714304046
   80/3780 0.050 0.104 0.1 MinMaxScaler()   0 -> 0.6558245616480888
   81/3780 0.050 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5624801890628168
   82/3780 0.050 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5922059327761656
   83/3780 0.050 0.104 1.0 StandardScaler() 0 -> 0.585701731336736
   84/3780 0.050 0.104 1.0 MinMaxScaler()   0 -> 0.5562764542041363
   85/3780 0.050 0.132 scale StandardScaler() 0 -> 0.5561560366983023
   86/3780 0.050 0.132 scale MinMaxScaler()   0 -> 0.5542223653920385
   87/3780 0.050 0.132 auto StandardScaler() 0 -> 0.5561560366983022
   88/3780 0.050 0.132 auto MinMaxScaler()   0 -> 0.6023603094949207
   89/3780 0.050 0.132 0.01 StandardScaler() 0 -> 0.5960537787456325
   90/3780 0.050 0.132 0.01 MinMaxScaler()   0 -> 0.7218356627183976
   91/3780 0.050 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5570280714257273
   92/3780 0.050 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6936550623565377
   93/3780 0.050 0.132 0.1 StandardScaler() 0 -> 0.5519234992045521
   94/3780 0.050 0.132 0.1 MinMaxScaler()   0 -> 0.6427407547472873
   95/3780 0.050 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5590601009063354
   96/3780 0.050 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5846228652004598
   97/3780 0.050 0.132 1.0 StandardScaler() 0 -> 0.5765007789116647
   98/3780 0.050 0.132 1.0 MinMaxScaler()   0 -> 0.5525580921805281
   99/3780 0.050 0.168 scale StandardScaler() 0 -> 0.5543054373313661
  100/3780 0.050 0.168 scale MinMaxScaler()   0 -> 0.5524379255975752
  101/3780 0.050 0.168 auto StandardScaler() 0 -> 0.5543054373316322
  102/3780 0.050 0.168 auto MinMaxScaler()   0 -> 0.592203620141286
  103/3780 0.050 0.168 0.01 StandardScaler() 0 -> 0.5874985506034092
  104/3780 0.050 0.168 0.01 MinMaxScaler()   0 -> 0.7168450712282382
  105/3780 0.050 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5527133615634353
  106/3780 0.050 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6857788918008407
  107/3780 0.050 0.168 0.1 StandardScaler() 0 -> 0.5510893927794308
  108/3780 0.050 0.168 0.1 MinMaxScaler()   0 -> 0.6279802990903319
  109/3780 0.050 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5558391574248952
  110/3780 0.050 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.579618442298491
  111/3780 0.050 0.168 1.0 StandardScaler() 0 -> 0.5681252086921313
  112/3780 0.050 0.168 1.0 MinMaxScaler()   0 -> 0.5503433242962376
  113/3780 0.050 0.213 scale StandardScaler() 0 -> 0.5527980773017093
  114/3780 0.050 0.213 scale MinMaxScaler()   0 -> 0.5509083869616948
  115/3780 0.050 0.213 auto StandardScaler() 0 -> 0.552798077301648
  116/3780 0.050 0.213 auto MinMaxScaler()   0 -> 0.5864667796100002
  117/3780 0.050 0.213 0.01 StandardScaler() 0 -> 0.5821031432357919
  118/3780 0.050 0.213 0.01 MinMaxScaler()   0 -> 0.7117600410795148
  119/3780 0.050 0.213 0.03162277660168379 StandardScaler() 0 -> 0.549979104692294
  120/3780 0.050 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6763731446743787
  121/3780 0.050 0.213 0.1 StandardScaler() 0 -> 0.551030694825076
  122/3780 0.050 0.213 0.1 MinMaxScaler()   0 -> 0.6134032238690418
  123/3780 0.050 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5534557786715428
  124/3780 0.050 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.574988515417746
  125/3780 0.050 0.213 1.0 StandardScaler() 0 -> 0.5607742770464929
  126/3780 0.050 0.213 1.0 MinMaxScaler()   0 -> 0.5486191366243421
  127/3780 0.050 0.270 scale StandardScaler() 0 -> 0.551047236202623
  128/3780 0.050 0.270 scale MinMaxScaler()   0 -> 0.5495534865650956
  129/3780 0.050 0.270 auto StandardScaler() 0 -> 0.5510472362025575
  130/3780 0.050 0.270 auto MinMaxScaler()   0 -> 0.5826068509534243
  131/3780 0.050 0.270 0.01 StandardScaler() 0 -> 0.5768307656723055
  132/3780 0.050 0.270 0.01 MinMaxScaler()   0 -> 0.7062005392925309
  133/3780 0.050 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5488508971761731
  134/3780 0.050 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6654562755033484
  135/3780 0.050 0.270 0.1 StandardScaler() 0 -> 0.5504764370746337
  136/3780 0.050 0.270 0.1 MinMaxScaler()   0 -> 0.6011678212748857
  137/3780 0.050 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5515805700482108
  138/3780 0.050 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5701320859756848
  139/3780 0.050 0.270 1.0 StandardScaler() 0 -> 0.5546046046859646
  140/3780 0.050 0.270 1.0 MinMaxScaler()   0 -> 0.5488311616098103
  141/3780 0.050 0.342 scale StandardScaler() 0 -> 0.5498195308136197
  142/3780 0.050 0.342 scale MinMaxScaler()   0 -> 0.5482797080999756
  143/3780 0.050 0.342 auto StandardScaler() 0 -> 0.5498195308109999
  144/3780 0.050 0.342 auto MinMaxScaler()   0 -> 0.5785826557595483
  145/3780 0.050 0.342 0.01 StandardScaler() 0 -> 0.5722538752940388
  146/3780 0.050 0.342 0.01 MinMaxScaler()   0 -> 0.6993823954415074
  147/3780 0.050 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5479395708535635
  148/3780 0.050 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.6528257040902247
  149/3780 0.050 0.342 0.1 StandardScaler() 0 -> 0.5502207130134021
  150/3780 0.050 0.342 0.1 MinMaxScaler()   0 -> 0.5933369326891708
  151/3780 0.050 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5497882751256651
  152/3780 0.050 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.565899864810315
  153/3780 0.050 0.342 1.0 StandardScaler() 0 -> 0.5504767445272786
  154/3780 0.050 0.342 1.0 MinMaxScaler()   0 -> 0.548891676069004
  155/3780 0.050 0.434 scale StandardScaler() 0 -> 0.5492835443520722
  156/3780 0.050 0.434 scale MinMaxScaler()   0 -> 0.5471684187008864
  157/3780 0.050 0.434 auto StandardScaler() 0 -> 0.549283544352209
  158/3780 0.050 0.434 auto MinMaxScaler()   0 -> 0.5750441615966486
  159/3780 0.050 0.434 0.01 StandardScaler() 0 -> 0.5681894860833345
  160/3780 0.050 0.434 0.01 MinMaxScaler()   0 -> 0.6924393317428045
  161/3780 0.050 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5482817397413683
  162/3780 0.050 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.6389810991729529
  163/3780 0.050 0.434 0.1 StandardScaler() 0 -> 0.5499845852605857
  164/3780 0.050 0.434 0.1 MinMaxScaler()   0 -> 0.5887825798299327
  165/3780 0.050 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5485243803125791
  166/3780 0.050 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.560931180906139
  167/3780 0.050 0.434 1.0 StandardScaler() 0 -> 0.5488662426826477
  168/3780 0.050 0.434 1.0 MinMaxScaler()   0 -> 0.5489857617257113
  169/3780 0.050 0.551 scale StandardScaler() 0 -> 0.5484477191603055
  170/3780 0.050 0.551 scale MinMaxScaler()   0 -> 0.5461552916503362
  171/3780 0.050 0.551 auto StandardScaler() 0 -> 0.5484477191603057
  172/3780 0.050 0.551 auto MinMaxScaler()   0 -> 0.571366172786405
  173/3780 0.050 0.551 0.01 StandardScaler() 0 -> 0.5633595401394073
  174/3780 0.050 0.551 0.01 MinMaxScaler()   0 -> 0.6842660295678877
  175/3780 0.050 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5481706515078364
  176/3780 0.050 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.6248983563201174
  177/3780 0.050 0.551 0.1 StandardScaler() 0 -> 0.5495105889418405
  178/3780 0.050 0.551 0.1 MinMaxScaler()   0 -> 0.5860027255093416
  179/3780 0.050 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5475377498107571
  180/3780 0.050 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5564641886801875
  181/3780 0.050 0.551 1.0 StandardScaler() 0 -> 0.5480125856420445
  182/3780 0.050 0.551 1.0 MinMaxScaler()   0 -> 0.5488337328695033
  183/3780 0.050 0.700 scale StandardScaler() 0 -> 0.5476975937000449
  184/3780 0.050 0.700 scale MinMaxScaler()   0 -> 0.5454163332417443
  185/3780 0.050 0.700 auto StandardScaler() 0 -> 0.5476975937022198
  186/3780 0.050 0.700 auto MinMaxScaler()   0 -> 0.5674270505549671
  187/3780 0.050 0.700 0.01 StandardScaler() 0 -> 0.5589391733966652
  188/3780 0.050 0.700 0.01 MinMaxScaler()   0 -> 0.6741823272595688
  189/3780 0.050 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5480966518487022
  190/3780 0.050 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.6108739021329698
  191/3780 0.050 0.700 0.1 StandardScaler() 0 -> 0.5489907590838015
  192/3780 0.050 0.700 0.1 MinMaxScaler()   0 -> 0.5834506491060482
  193/3780 0.050 0.700 0.31622776601683794 StandardScaler() 0 -> 0.546777154794548
  194/3780 0.050 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5534905482706677
  195/3780 0.050 0.700 1.0 StandardScaler() 0 -> 0.5494068286451359
  196/3780 0.050 0.700 1.0 MinMaxScaler()   0 -> 0.5492761557397401
  197/3780 0.050 0.888 scale StandardScaler() 0 -> 0.5468716769365259
  198/3780 0.050 0.888 scale MinMaxScaler()   0 -> 0.5448562203754336
  199/3780 0.050 0.888 auto StandardScaler() 0 -> 0.5468716769365256
  200/3780 0.050 0.888 auto MinMaxScaler()   0 -> 0.5632241460921966
  201/3780 0.050 0.888 0.01 StandardScaler() 0 -> 0.5550659748543749
  202/3780 0.050 0.888 0.01 MinMaxScaler()   0 -> 0.6632996052503817
  203/3780 0.050 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5490266968252299
  204/3780 0.050 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5995874257496939
  205/3780 0.050 0.888 0.1 StandardScaler() 0 -> 0.5486753845086476
  206/3780 0.050 0.888 0.1 MinMaxScaler()   0 -> 0.5812951702519262
  207/3780 0.050 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5471961033606138
  208/3780 0.050 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5517327029023432
  209/3780 0.050 0.888 1.0 StandardScaler() 0 -> 0.5541135039404456
  210/3780 0.050 0.888 1.0 MinMaxScaler()   0 -> 0.5493420550701312
  211/3780 0.050 1.126 scale StandardScaler() 0 -> 0.5473283363103696
  212/3780 0.050 1.126 scale MinMaxScaler()   0 -> 0.5443575036264515
  213/3780 0.050 1.126 auto StandardScaler() 0 -> 0.5473283363084631
  214/3780 0.050 1.126 auto MinMaxScaler()   0 -> 0.5590378421210453
  215/3780 0.050 1.126 0.01 StandardScaler() 0 -> 0.5525849697491002
  216/3780 0.050 1.126 0.01 MinMaxScaler()   0 -> 0.6503630160561605
  217/3780 0.050 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5490682248273185
  218/3780 0.050 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.593996636381438
  219/3780 0.050 1.126 0.1 StandardScaler() 0 -> 0.5488801196940751
  220/3780 0.050 1.126 0.1 MinMaxScaler()   0 -> 0.579035752265042
  221/3780 0.050 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5488889703964509
  222/3780 0.050 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5509853569116955
  223/3780 0.050 1.126 1.0 StandardScaler() 0 -> 0.5609323675589378
  224/3780 0.050 1.126 1.0 MinMaxScaler()   0 -> 0.5495079747173262
  225/3780 0.050 1.429 scale StandardScaler() 0 -> 0.5485106644237613
  226/3780 0.050 1.429 scale MinMaxScaler()   0 -> 0.5448786209671368
  227/3780 0.050 1.429 auto StandardScaler() 0 -> 0.548501942402054
  228/3780 0.050 1.429 auto MinMaxScaler()   0 -> 0.5552863448965566
  229/3780 0.050 1.429 0.01 StandardScaler() 0 -> 0.5506595720062927
  230/3780 0.050 1.429 0.01 MinMaxScaler()   0 -> 0.6360729232069181
  231/3780 0.050 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5489811019145666
  232/3780 0.050 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5909692581416012
  233/3780 0.050 1.429 0.1 StandardScaler() 0 -> 0.5485170711265323
  234/3780 0.050 1.429 0.1 MinMaxScaler()   0 -> 0.5762387697657942
  235/3780 0.050 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5508849003739231
  236/3780 0.050 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5501609433798423
  237/3780 0.050 1.429 1.0 StandardScaler() 0 -> 0.5693623809108382
  238/3780 0.050 1.429 1.0 MinMaxScaler()   0 -> 0.5496525330627855
  239/3780 0.050 1.814 scale StandardScaler() 0 -> 0.5496460408731769
  240/3780 0.050 1.814 scale MinMaxScaler()   0 -> 0.5456527438520732
  241/3780 0.050 1.814 auto StandardScaler() 0 -> 0.5496320714925446
  242/3780 0.050 1.814 auto MinMaxScaler()   0 -> 0.5526901686464298
  243/3780 0.050 1.814 0.01 StandardScaler() 0 -> 0.5501000888462003
  244/3780 0.050 1.814 0.01 MinMaxScaler()   0 -> 0.62229506011733
  245/3780 0.050 1.814 0.03162277660168379 StandardScaler() 0 -> 0.549378201759574
  246/3780 0.050 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5894636964666685
  247/3780 0.050 1.814 0.1 StandardScaler() 0 -> 0.5486432420322518
  248/3780 0.050 1.814 0.1 MinMaxScaler()   0 -> 0.5728620412299964
  249/3780 0.050 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5537323230628927
  250/3780 0.050 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5502202002544646
  251/3780 0.050 1.814 1.0 StandardScaler() 0 -> 0.5799035825936772
  252/3780 0.050 1.814 1.0 MinMaxScaler()   0 -> 0.5497408198444443
  253/3780 0.050 2.302 scale StandardScaler() 0 -> 0.5518602872832716
  254/3780 0.050 2.302 scale MinMaxScaler()   0 -> 0.547789047068399
  255/3780 0.050 2.302 auto StandardScaler() 0 -> 0.551860287268556
  256/3780 0.050 2.302 auto MinMaxScaler()   0 -> 0.551862336469921
  257/3780 0.050 2.302 0.01 StandardScaler() 0 -> 0.5499461807177964
  258/3780 0.050 2.302 0.01 MinMaxScaler()   0 -> 0.6088000612307406
  259/3780 0.050 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5489751184657948
  260/3780 0.050 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5881084146630259
  261/3780 0.050 2.302 0.1 StandardScaler() 0 -> 0.5491374535310513
  262/3780 0.050 2.302 0.1 MinMaxScaler()   0 -> 0.5693711203001013
  263/3780 0.050 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5575262334828323
  264/3780 0.050 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5506934370081661
  265/3780 0.050 2.302 1.0 StandardScaler() 0 -> 0.5919297334941321
  266/3780 0.050 2.302 1.0 MinMaxScaler()   0 -> 0.5496303974558917
  267/3780 0.050 2.921 scale StandardScaler() 0 -> 0.5546017244691231
  268/3780 0.050 2.921 scale MinMaxScaler()   0 -> 0.5496810577448117
  269/3780 0.050 2.921 auto StandardScaler() 0 -> 0.5546018643978613
  270/3780 0.050 2.921 auto MinMaxScaler()   0 -> 0.5511183171933823
  271/3780 0.050 2.921 0.01 StandardScaler() 0 -> 0.550030914050211
  272/3780 0.050 2.921 0.01 MinMaxScaler()   0 -> 0.5982762182786501
  273/3780 0.050 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5487953234290286
  274/3780 0.050 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5872448534931025
  275/3780 0.050 2.921 0.1 StandardScaler() 0 -> 0.5484959562070965
  276/3780 0.050 2.921 0.1 MinMaxScaler()   0 -> 0.565373399867049
  277/3780 0.050 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5622490264203533
  278/3780 0.050 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5508321316909034
  279/3780 0.050 2.921 1.0 StandardScaler() 0 -> 0.607273097738402
  280/3780 0.050 2.921 1.0 MinMaxScaler()   0 -> 0.5495191298129259
  281/3780 0.050 3.707 scale StandardScaler() 0 -> 0.5568100058677002
  282/3780 0.050 3.707 scale MinMaxScaler()   0 -> 0.5518658990609243
  283/3780 0.050 3.707 auto StandardScaler() 0 -> 0.556807916469538
  284/3780 0.050 3.707 auto MinMaxScaler()   0 -> 0.5506912709734441
  285/3780 0.050 3.707 0.01 StandardScaler() 0 -> 0.5505448506704497
  286/3780 0.050 3.707 0.01 MinMaxScaler()   0 -> 0.5935194212706977
  287/3780 0.050 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5485969033643437
  288/3780 0.050 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5862013957052704
  289/3780 0.050 3.707 0.1 StandardScaler() 0 -> 0.5489046212567481
  290/3780 0.050 3.707 0.1 MinMaxScaler()   0 -> 0.5614603591079675
  291/3780 0.050 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5679312961959098
  292/3780 0.050 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5511623526705033
  293/3780 0.050 3.707 1.0 StandardScaler() 0 -> 0.6238560803055196
  294/3780 0.050 3.707 1.0 MinMaxScaler()   0 -> 0.5493752697632536
  295/3780 0.050 4.703 scale StandardScaler() 0 -> 0.5592555570009808
  296/3780 0.050 4.703 scale MinMaxScaler()   0 -> 0.5555697853103365
  297/3780 0.050 4.703 auto StandardScaler() 0 -> 0.5592603597788691
  298/3780 0.050 4.703 auto MinMaxScaler()   0 -> 0.5510332922132963
  299/3780 0.050 4.703 0.01 StandardScaler() 0 -> 0.5505443569863627
  300/3780 0.050 4.703 0.01 MinMaxScaler()   0 -> 0.5916737044128199
  301/3780 0.050 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5485365717633924
  302/3780 0.050 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5850851029263052
  303/3780 0.050 4.703 0.1 StandardScaler() 0 -> 0.5495865947820892
  304/3780 0.050 4.703 0.1 MinMaxScaler()   0 -> 0.5570431803175956
  305/3780 0.050 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5738329322472687
  306/3780 0.050 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5515549404276888
  307/3780 0.050 4.703 1.0 StandardScaler() 0 -> 0.6391028445561284
  308/3780 0.050 4.703 1.0 MinMaxScaler()   0 -> 0.5489470823258032
  309/3780 0.050 5.968 scale StandardScaler() 0 -> 0.5626749432251662
  310/3780 0.050 5.968 scale MinMaxScaler()   0 -> 0.5592451562194428
  311/3780 0.050 5.968 auto StandardScaler() 0 -> 0.5626804191791552
  312/3780 0.050 5.968 auto MinMaxScaler()   0 -> 0.5513749841848373
  313/3780 0.050 5.968 0.01 StandardScaler() 0 -> 0.5509503286208688
  314/3780 0.050 5.968 0.01 MinMaxScaler()   0 -> 0.5906278632866186
  315/3780 0.050 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5484561142745318
  316/3780 0.050 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5840987873815208
  317/3780 0.050 5.968 0.1 StandardScaler() 0 -> 0.5506328346053039
  318/3780 0.050 5.968 0.1 MinMaxScaler()   0 -> 0.5542179781398636
  319/3780 0.050 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5781807418515933
  320/3780 0.050 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5518692534439368
  321/3780 0.050 5.968 1.0 StandardScaler() 0 -> 0.6584454695278948
  322/3780 0.050 5.968 1.0 MinMaxScaler()   0 -> 0.5492997625311754
  323/3780 0.050 7.574 scale StandardScaler() 0 -> 0.5678053264970006
  324/3780 0.050 7.574 scale MinMaxScaler()   0 -> 0.5624179619615748
  325/3780 0.050 7.574 auto StandardScaler() 0 -> 0.5678108214330684
  326/3780 0.050 7.574 auto MinMaxScaler()   0 -> 0.5517022259423804
  327/3780 0.050 7.574 0.01 StandardScaler() 0 -> 0.5508684069048878
  328/3780 0.050 7.574 0.01 MinMaxScaler()   0 -> 0.589976534202516
  329/3780 0.050 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5488824800281643
  330/3780 0.050 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5824990110496936
  331/3780 0.050 7.574 0.1 StandardScaler() 0 -> 0.5518963120524005
  332/3780 0.050 7.574 0.1 MinMaxScaler()   0 -> 0.5523105803703272
  333/3780 0.050 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5845891788320651
  334/3780 0.050 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5518566880369343
  335/3780 0.050 7.574 1.0 StandardScaler() 0 -> 0.6787531208416558
  336/3780 0.050 7.574 1.0 MinMaxScaler()   0 -> 0.5493687775085455
  337/3780 0.050 9.611 scale StandardScaler() 0 -> 0.5728953601456815
  338/3780 0.050 9.611 scale MinMaxScaler()   0 -> 0.5662863081763111
  339/3780 0.050 9.611 auto StandardScaler() 0 -> 0.5728947699800103
  340/3780 0.050 9.611 auto MinMaxScaler()   0 -> 0.5521059884998364
  341/3780 0.050 9.611 0.01 StandardScaler() 0 -> 0.5509382275081722
  342/3780 0.050 9.611 0.01 MinMaxScaler()   0 -> 0.5898480404875633
  343/3780 0.050 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5492368410578924
  344/3780 0.050 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5803734407747556
  345/3780 0.050 9.611 0.1 StandardScaler() 0 -> 0.553565063553335
  346/3780 0.050 9.611 0.1 MinMaxScaler()   0 -> 0.5516396158537223
  347/3780 0.050 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5929748467483917
  348/3780 0.050 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5517382074131376
  349/3780 0.050 9.611 1.0 StandardScaler() 0 -> 0.6987451446560723
  350/3780 0.050 9.611 1.0 MinMaxScaler()   0 -> 0.549336372351607
  351/3780 0.050 12.196 scale StandardScaler() 0 -> 0.5792773436508251
  352/3780 0.050 12.196 scale MinMaxScaler()   0 -> 0.5716277917814085
  353/3780 0.050 12.196 auto StandardScaler() 0 -> 0.5792801739300358
  354/3780 0.050 12.196 auto MinMaxScaler()   0 -> 0.5528590884076682
  355/3780 0.050 12.196 0.01 StandardScaler() 0 -> 0.5508290073142211
  356/3780 0.050 12.196 0.01 MinMaxScaler()   0 -> 0.5894472476235415
  357/3780 0.050 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5497286034778143
  358/3780 0.050 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5778518758118344
  359/3780 0.050 12.196 0.1 StandardScaler() 0 -> 0.5552242238358868
  360/3780 0.050 12.196 0.1 MinMaxScaler()   0 -> 0.551447251007673
  361/3780 0.050 12.196 0.31622776601683794 StandardScaler() 0 -> 0.6031048226272627
  362/3780 0.050 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5514173533812453
  363/3780 0.050 12.196 1.0 StandardScaler() 0 -> 0.7233036012738201
  364/3780 0.050 12.196 1.0 MinMaxScaler()   0 -> 0.5492705330720901
  365/3780 0.050 15.476 scale StandardScaler() 0 -> 0.5853736552393297
  366/3780 0.050 15.476 scale MinMaxScaler()   0 -> 0.5777830998572068
  367/3780 0.050 15.476 auto StandardScaler() 0 -> 0.5853762045372748
  368/3780 0.050 15.476 auto MinMaxScaler()   0 -> 0.5530315366831038
  369/3780 0.050 15.476 0.01 StandardScaler() 0 -> 0.5510194073626633
  370/3780 0.050 15.476 0.01 MinMaxScaler()   0 -> 0.589165962048816
  371/3780 0.050 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5503447783544549
  372/3780 0.050 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5749289358795673
  373/3780 0.050 15.476 0.1 StandardScaler() 0 -> 0.5579087092814111
  374/3780 0.050 15.476 0.1 MinMaxScaler()   0 -> 0.5514308598298069
  375/3780 0.050 15.476 0.31622776601683794 StandardScaler() 0 -> 0.6138134396711052
  376/3780 0.050 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5515327706250378
  377/3780 0.050 15.476 1.0 StandardScaler() 0 -> 0.7496044862943765
  378/3780 0.050 15.476 1.0 MinMaxScaler()   0 -> 0.5490831120828628
  379/3780 0.050 19.638 scale StandardScaler() 0 -> 0.593904748024081
  380/3780 0.050 19.638 scale MinMaxScaler()   0 -> 0.5840115816998902
  381/3780 0.050 19.638 auto StandardScaler() 0 -> 0.5939013115966522
  382/3780 0.050 19.638 auto MinMaxScaler()   0 -> 0.5531037372808654
  383/3780 0.050 19.638 0.01 StandardScaler() 0 -> 0.5509982051842699
  384/3780 0.050 19.638 0.01 MinMaxScaler()   0 -> 0.5892791333321439
  385/3780 0.050 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5505293305035545
  386/3780 0.050 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5714353640170118
  387/3780 0.050 19.638 0.1 StandardScaler() 0 -> 0.5602542806809897
  388/3780 0.050 19.638 0.1 MinMaxScaler()   0 -> 0.5517051392593234
  389/3780 0.050 19.638 0.31622776601683794 StandardScaler() 0 -> 0.6258095830364301
  390/3780 0.050 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5514789838868235
  391/3780 0.050 19.638 1.0 StandardScaler() 0 -> 0.7796167333687282
  392/3780 0.050 19.638 1.0 MinMaxScaler()   0 -> 0.549246660722511
  393/3780 0.050 24.920 scale StandardScaler() 0 -> 0.6029749356302379
  394/3780 0.050 24.920 scale MinMaxScaler()   0 -> 0.5890298637791725
  395/3780 0.050 24.920 auto StandardScaler() 0 -> 0.6029673978566928
  396/3780 0.050 24.920 auto MinMaxScaler()   0 -> 0.553301984654956
  397/3780 0.050 24.920 0.01 StandardScaler() 0 -> 0.5511644929544016
  398/3780 0.050 24.920 0.01 MinMaxScaler()   0 -> 0.5887346289588989
  399/3780 0.050 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5508340890709112
  400/3780 0.050 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.568088723963737
  401/3780 0.050 24.920 0.1 StandardScaler() 0 -> 0.5631121359618371
  402/3780 0.050 24.920 0.1 MinMaxScaler()   0 -> 0.551900412972261
  403/3780 0.050 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6405804253756336
  404/3780 0.050 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5513010242312969
  405/3780 0.050 24.920 1.0 StandardScaler() 0 -> 0.8151791718823836
  406/3780 0.050 24.920 1.0 MinMaxScaler()   0 -> 0.5489521200159432
  407/3780 0.050 31.623 scale StandardScaler() 0 -> 0.6099220910083915
  408/3780 0.050 31.623 scale MinMaxScaler()   0 -> 0.5947370254705244
  409/3780 0.050 31.623 auto StandardScaler() 0 -> 0.6099225201252482
  410/3780 0.050 31.623 auto MinMaxScaler()   0 -> 0.5527830715300204
  411/3780 0.050 31.623 0.01 StandardScaler() 0 -> 0.5512518679401602
  412/3780 0.050 31.623 0.01 MinMaxScaler()   0 -> 0.5880621208028962
  413/3780 0.050 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5510493355759044
  414/3780 0.050 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5638717644916066
  415/3780 0.050 31.623 0.1 StandardScaler() 0 -> 0.5656075411112065
  416/3780 0.050 31.623 0.1 MinMaxScaler()   0 -> 0.552338019801275
  417/3780 0.050 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6586201223311631
  418/3780 0.050 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5515445770140893
  419/3780 0.050 31.623 1.0 StandardScaler() 0 -> 0.8552735655514533
  420/3780 0.050 31.623 1.0 MinMaxScaler()   0 -> 0.5494605001142817
  421/3780 0.100 0.032 scale StandardScaler() 0 -> 0.5712277290168506
  422/3780 0.100 0.032 scale MinMaxScaler()   0 -> 0.5666257830743092
  423/3780 0.100 0.032 auto StandardScaler() 0 -> 0.5712277290168505
  424/3780 0.100 0.032 auto MinMaxScaler()   0 -> 0.6561376352519098
  425/3780 0.100 0.032 0.01 StandardScaler() 0 -> 0.6516246910812615
  426/3780 0.100 0.032 0.01 MinMaxScaler()   0 -> 0.7425824094239554
  427/3780 0.100 0.032 0.03162277660168379 StandardScaler() 0 -> 0.598869700172764
  428/3780 0.100 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7166569802735641
  429/3780 0.100 0.032 0.1 StandardScaler() 0 -> 0.5678084325447669
  430/3780 0.100 0.032 0.1 MinMaxScaler()   0 -> 0.6811072141476466
  431/3780 0.100 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5802943639304549
  432/3780 0.100 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6372726808293194
  433/3780 0.100 0.032 1.0 StandardScaler() 0 -> 0.6352619563891242
  434/3780 0.100 0.032 1.0 MinMaxScaler()   0 -> 0.5859748469721904
  435/3780 0.100 0.040 scale StandardScaler() 0 -> 0.5647419829322177
  436/3780 0.100 0.040 scale MinMaxScaler()   0 -> 0.5608306497831975
  437/3780 0.100 0.040 auto StandardScaler() 0 -> 0.5647419829322176
  438/3780 0.100 0.040 auto MinMaxScaler()   0 -> 0.6458285690347721
  439/3780 0.100 0.040 0.01 StandardScaler() 0 -> 0.6410533445954655
  440/3780 0.100 0.040 0.01 MinMaxScaler()   0 -> 0.7390801393637988
  441/3780 0.100 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5872616202535106
  442/3780 0.100 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.707897661262772
  443/3780 0.100 0.040 0.1 StandardScaler() 0 -> 0.560476822310092
  444/3780 0.100 0.040 0.1 MinMaxScaler()   0 -> 0.6723470940403121
  445/3780 0.100 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5730047817798102
  446/3780 0.100 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.6263040818898414
  447/3780 0.100 0.040 1.0 StandardScaler() 0 -> 0.6221735287238954
  448/3780 0.100 0.040 1.0 MinMaxScaler()   0 -> 0.5771270049303147
  449/3780 0.100 0.051 scale StandardScaler() 0 -> 0.5598975671010131
  450/3780 0.100 0.051 scale MinMaxScaler()   0 -> 0.5570219256235358
  451/3780 0.100 0.051 auto StandardScaler() 0 -> 0.5598975671010131
  452/3780 0.100 0.051 auto MinMaxScaler()   0 -> 0.6352150195782054
  453/3780 0.100 0.051 0.01 StandardScaler() 0 -> 0.6291989048604848
  454/3780 0.100 0.051 0.01 MinMaxScaler()   0 -> 0.7347346136289642
  455/3780 0.100 0.051 0.03162277660168379 StandardScaler() 0 -> 0.578968031959084
  456/3780 0.100 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6999480234247039
  457/3780 0.100 0.051 0.1 StandardScaler() 0 -> 0.5552857534821136
  458/3780 0.100 0.051 0.1 MinMaxScaler()   0 -> 0.6636674122002924
  459/3780 0.100 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5673823796846424
  460/3780 0.100 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.6135615275975561
  461/3780 0.100 0.051 1.0 StandardScaler() 0 -> 0.6105379224774855
  462/3780 0.100 0.051 1.0 MinMaxScaler()   0 -> 0.5700274589956168
  463/3780 0.100 0.065 scale StandardScaler() 0 -> 0.5567841013238579
  464/3780 0.100 0.065 scale MinMaxScaler()   0 -> 0.5546616610469366
  465/3780 0.100 0.065 auto StandardScaler() 0 -> 0.5567841013238578
  466/3780 0.100 0.065 auto MinMaxScaler()   0 -> 0.6237818842416477
  467/3780 0.100 0.065 0.01 StandardScaler() 0 -> 0.617879627929228
  468/3780 0.100 0.065 0.01 MinMaxScaler()   0 -> 0.7293792734145851
  469/3780 0.100 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5713336084982609
  470/3780 0.100 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.693493811816825
  471/3780 0.100 0.065 0.1 StandardScaler() 0 -> 0.5521052015219664
  472/3780 0.100 0.065 0.1 MinMaxScaler()   0 -> 0.6540512395872978
  473/3780 0.100 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5628803087682578
  474/3780 0.100 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.6018739280231556
  475/3780 0.100 0.065 1.0 StandardScaler() 0 -> 0.5992103248009079
  476/3780 0.100 0.065 1.0 MinMaxScaler()   0 -> 0.5635944646100975
  477/3780 0.100 0.082 scale StandardScaler() 0 -> 0.5547908098826643
  478/3780 0.100 0.082 scale MinMaxScaler()   0 -> 0.5529196560728867
  479/3780 0.100 0.082 auto StandardScaler() 0 -> 0.5547908098826642
  480/3780 0.100 0.082 auto MinMaxScaler()   0 -> 0.6113107215350774
  481/3780 0.100 0.082 0.01 StandardScaler() 0 -> 0.6058331720752878
  482/3780 0.100 0.082 0.01 MinMaxScaler()   0 -> 0.7228395267714279
  483/3780 0.100 0.082 0.03162277660168379 StandardScaler() 0 -> 0.564760009613284
  484/3780 0.100 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6865625253092325
  485/3780 0.100 0.082 0.1 StandardScaler() 0 -> 0.5507459540213411
  486/3780 0.100 0.082 0.1 MinMaxScaler()   0 -> 0.6436068844805595
  487/3780 0.100 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5588779142960889
  488/3780 0.100 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5927778384642902
  489/3780 0.100 0.082 1.0 StandardScaler() 0 -> 0.5885344117115475
  490/3780 0.100 0.082 1.0 MinMaxScaler()   0 -> 0.5580802962056034
  491/3780 0.100 0.104 scale StandardScaler() 0 -> 0.5526100788993249
  492/3780 0.100 0.104 scale MinMaxScaler()   0 -> 0.551015887951701
  493/3780 0.100 0.104 auto StandardScaler() 0 -> 0.5526100788993246
  494/3780 0.100 0.104 auto MinMaxScaler()   0 -> 0.6008490482151446
  495/3780 0.100 0.104 0.01 StandardScaler() 0 -> 0.5958946982859418
  496/3780 0.100 0.104 0.01 MinMaxScaler()   0 -> 0.7149530240908
  497/3780 0.100 0.104 0.03162277660168379 StandardScaler() 0 -> 0.559342602932542
  498/3780 0.100 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6784957069602527
  499/3780 0.100 0.104 0.1 StandardScaler() 0 -> 0.5490669139338548
  500/3780 0.100 0.104 0.1 MinMaxScaler()   0 -> 0.6330484133023919
  501/3780 0.100 0.104 0.31622776601683794 StandardScaler() 0 -> 0.555867580738987
  502/3780 0.100 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5865782906843585
  503/3780 0.100 0.104 1.0 StandardScaler() 0 -> 0.5789003230138218
  504/3780 0.100 0.104 1.0 MinMaxScaler()   0 -> 0.5531745013339262
  505/3780 0.100 0.132 scale StandardScaler() 0 -> 0.5507267310639243
  506/3780 0.100 0.132 scale MinMaxScaler()   0 -> 0.5496165118212346
  507/3780 0.100 0.132 auto StandardScaler() 0 -> 0.5507267310639242
  508/3780 0.100 0.132 auto MinMaxScaler()   0 -> 0.5930502954664603
  509/3780 0.100 0.132 0.01 StandardScaler() 0 -> 0.5890243086709204
  510/3780 0.100 0.132 0.01 MinMaxScaler()   0 -> 0.7061216242274728
  511/3780 0.100 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5540190242058142
  512/3780 0.100 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6700324057190086
  513/3780 0.100 0.132 0.1 StandardScaler() 0 -> 0.5479232624531069
  514/3780 0.100 0.132 0.1 MinMaxScaler()   0 -> 0.6216428535503081
  515/3780 0.100 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5532251624813803
  516/3780 0.100 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5812628620105683
  517/3780 0.100 0.132 1.0 StandardScaler() 0 -> 0.570094132036269
  518/3780 0.100 0.132 1.0 MinMaxScaler()   0 -> 0.5498671778465805
  519/3780 0.100 0.168 scale StandardScaler() 0 -> 0.5489228989618608
  520/3780 0.100 0.168 scale MinMaxScaler()   0 -> 0.5480492045675275
  521/3780 0.100 0.168 auto StandardScaler() 0 -> 0.5489228989618606
  522/3780 0.100 0.168 auto MinMaxScaler()   0 -> 0.5875165849819481
  523/3780 0.100 0.168 0.01 StandardScaler() 0 -> 0.5839926508808442
  524/3780 0.100 0.168 0.01 MinMaxScaler()   0 -> 0.6985619647711862
  525/3780 0.100 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5499122477182413
  526/3780 0.100 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6616078932321972
  527/3780 0.100 0.168 0.1 StandardScaler() 0 -> 0.5470793168607814
  528/3780 0.100 0.168 0.1 MinMaxScaler()   0 -> 0.6097381971183432
  529/3780 0.100 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5508305855823568
  530/3780 0.100 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5772533589299226
  531/3780 0.100 0.168 1.0 StandardScaler() 0 -> 0.562601333679175
  532/3780 0.100 0.168 1.0 MinMaxScaler()   0 -> 0.5482648191042586
  533/3780 0.100 0.213 scale StandardScaler() 0 -> 0.5477153667166031
  534/3780 0.100 0.213 scale MinMaxScaler()   0 -> 0.5461506122133971
  535/3780 0.100 0.213 auto StandardScaler() 0 -> 0.547715366716603
  536/3780 0.100 0.213 auto MinMaxScaler()   0 -> 0.5840675058259657
  537/3780 0.100 0.213 0.01 StandardScaler() 0 -> 0.5794029950201202
  538/3780 0.100 0.213 0.01 MinMaxScaler()   0 -> 0.6919681172198883
  539/3780 0.100 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5482183074574278
  540/3780 0.100 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6515784060313551
  541/3780 0.100 0.213 0.1 StandardScaler() 0 -> 0.5466876786593627
  542/3780 0.100 0.213 0.1 MinMaxScaler()   0 -> 0.6000238746343302
  543/3780 0.100 0.213 0.31622776601683794 StandardScaler() 0 -> 0.548592178951368
  544/3780 0.100 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5730101988991902
  545/3780 0.100 0.213 1.0 StandardScaler() 0 -> 0.556011751931889
  546/3780 0.100 0.213 1.0 MinMaxScaler()   0 -> 0.5474342961385341
  547/3780 0.100 0.270 scale StandardScaler() 0 -> 0.5465753704761608
  548/3780 0.100 0.270 scale MinMaxScaler()   0 -> 0.5452083834897957
  549/3780 0.100 0.270 auto StandardScaler() 0 -> 0.5465753704761607
  550/3780 0.100 0.270 auto MinMaxScaler()   0 -> 0.5806082180479732
  551/3780 0.100 0.270 0.01 StandardScaler() 0 -> 0.5752201271778757
  552/3780 0.100 0.270 0.01 MinMaxScaler()   0 -> 0.6852249728677743
  553/3780 0.100 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5475516889295439
  554/3780 0.100 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6413590979740635
  555/3780 0.100 0.270 0.1 StandardScaler() 0 -> 0.5464731459483628
  556/3780 0.100 0.270 0.1 MinMaxScaler()   0 -> 0.5938527283435188
  557/3780 0.100 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5461632621185684
  558/3780 0.100 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.568815660765591
  559/3780 0.100 0.270 1.0 StandardScaler() 0 -> 0.5506451446167113
  560/3780 0.100 0.270 1.0 MinMaxScaler()   0 -> 0.546657111320759
  561/3780 0.100 0.342 scale StandardScaler() 0 -> 0.5454943638693345
  562/3780 0.100 0.342 scale MinMaxScaler()   0 -> 0.5441444022482825
  563/3780 0.100 0.342 auto StandardScaler() 0 -> 0.5454943638710313
  564/3780 0.100 0.342 auto MinMaxScaler()   0 -> 0.5773027893531554
  565/3780 0.100 0.342 0.01 StandardScaler() 0 -> 0.5709305565151698
  566/3780 0.100 0.342 0.01 MinMaxScaler()   0 -> 0.6769787818734696
  567/3780 0.100 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5469265415148538
  568/3780 0.100 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.6307303296522343
  569/3780 0.100 0.342 0.1 StandardScaler() 0 -> 0.5465292025977057
  570/3780 0.100 0.342 0.1 MinMaxScaler()   0 -> 0.5893501094935002
  571/3780 0.100 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5447043299071382
  572/3780 0.100 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5644401705418786
  573/3780 0.100 0.342 1.0 StandardScaler() 0 -> 0.5474404216879515
  574/3780 0.100 0.342 1.0 MinMaxScaler()   0 -> 0.5463517663896499
  575/3780 0.100 0.434 scale StandardScaler() 0 -> 0.5446355523531037
  576/3780 0.100 0.434 scale MinMaxScaler()   0 -> 0.5429083132729241
  577/3780 0.100 0.434 auto StandardScaler() 0 -> 0.5446355523575735
  578/3780 0.100 0.434 auto MinMaxScaler()   0 -> 0.5740021767942608
  579/3780 0.100 0.434 0.01 StandardScaler() 0 -> 0.5662507711833921
  580/3780 0.100 0.434 0.01 MinMaxScaler()   0 -> 0.6685386594865562
  581/3780 0.100 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5463363192366947
  582/3780 0.100 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.6188379851460923
  583/3780 0.100 0.434 0.1 StandardScaler() 0 -> 0.5464558980214205
  584/3780 0.100 0.434 0.1 MinMaxScaler()   0 -> 0.587052567582501
  585/3780 0.100 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5437920348251473
  586/3780 0.100 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5607142179685137
  587/3780 0.100 0.434 1.0 StandardScaler() 0 -> 0.5461792919305307
  588/3780 0.100 0.434 1.0 MinMaxScaler()   0 -> 0.5458938195845968
  589/3780 0.100 0.551 scale StandardScaler() 0 -> 0.5436662384274092
  590/3780 0.100 0.551 scale MinMaxScaler()   0 -> 0.5415273990602492
  591/3780 0.100 0.551 auto StandardScaler() 0 -> 0.5436662384378486
  592/3780 0.100 0.551 auto MinMaxScaler()   0 -> 0.5705735797158358
  593/3780 0.100 0.551 0.01 StandardScaler() 0 -> 0.5620291387236572
  594/3780 0.100 0.551 0.01 MinMaxScaler()   0 -> 0.6596293618573187
  595/3780 0.100 0.551 0.03162277660168379 StandardScaler() 0 -> 0.54580552534856
  596/3780 0.100 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.6084017442873942
  597/3780 0.100 0.551 0.1 StandardScaler() 0 -> 0.5463590520105256
  598/3780 0.100 0.551 0.1 MinMaxScaler()   0 -> 0.5849321611706424
  599/3780 0.100 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5436243282039724
  600/3780 0.100 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5563190070399441
  601/3780 0.100 0.551 1.0 StandardScaler() 0 -> 0.5464247807203358
  602/3780 0.100 0.551 1.0 MinMaxScaler()   0 -> 0.5456436034961426
  603/3780 0.100 0.700 scale StandardScaler() 0 -> 0.5425924133363286
  604/3780 0.100 0.700 scale MinMaxScaler()   0 -> 0.5410291800813953
  605/3780 0.100 0.700 auto StandardScaler() 0 -> 0.5425958826992873
  606/3780 0.100 0.700 auto MinMaxScaler()   0 -> 0.5668136554633274
  607/3780 0.100 0.700 0.01 StandardScaler() 0 -> 0.5579905521146614
  608/3780 0.100 0.700 0.01 MinMaxScaler()   0 -> 0.64934278222089
  609/3780 0.100 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5456967387265467
  610/3780 0.100 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5993114150651714
  611/3780 0.100 0.700 0.1 StandardScaler() 0 -> 0.5463609278633169
  612/3780 0.100 0.700 0.1 MinMaxScaler()   0 -> 0.5827866424437153
  613/3780 0.100 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5434647721952129
  614/3780 0.100 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5528234339888211
  615/3780 0.100 0.700 1.0 StandardScaler() 0 -> 0.5488800403797559
  616/3780 0.100 0.700 1.0 MinMaxScaler()   0 -> 0.5456827649509733
  617/3780 0.100 0.888 scale StandardScaler() 0 -> 0.542838934290954
  618/3780 0.100 0.888 scale MinMaxScaler()   0 -> 0.5411426063701893
  619/3780 0.100 0.888 auto StandardScaler() 0 -> 0.5428389342791634
  620/3780 0.100 0.888 auto MinMaxScaler()   0 -> 0.5630765008303128
  621/3780 0.100 0.888 0.01 StandardScaler() 0 -> 0.554186791398885
  622/3780 0.100 0.888 0.01 MinMaxScaler()   0 -> 0.6394320018549308
  623/3780 0.100 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5453076155154742
  624/3780 0.100 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.593795839500052
  625/3780 0.100 0.888 0.1 StandardScaler() 0 -> 0.5459795408249963
  626/3780 0.100 0.888 0.1 MinMaxScaler()   0 -> 0.5802518220170914
  627/3780 0.100 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5444121726274498
  628/3780 0.100 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5507281732751598
  629/3780 0.100 0.888 1.0 StandardScaler() 0 -> 0.5534359615534931
  630/3780 0.100 0.888 1.0 MinMaxScaler()   0 -> 0.545061739303703
  631/3780 0.100 1.126 scale StandardScaler() 0 -> 0.5442191752136641
  632/3780 0.100 1.126 scale MinMaxScaler()   0 -> 0.5417983535960863
  633/3780 0.100 1.126 auto StandardScaler() 0 -> 0.5442191752136639
  634/3780 0.100 1.126 auto MinMaxScaler()   0 -> 0.558593778428711
  635/3780 0.100 1.126 0.01 StandardScaler() 0 -> 0.5515132770339367
  636/3780 0.100 1.126 0.01 MinMaxScaler()   0 -> 0.6286107780744613
  637/3780 0.100 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5451179894790595
  638/3780 0.100 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5910925683549453
  639/3780 0.100 1.126 0.1 StandardScaler() 0 -> 0.5456099089174802
  640/3780 0.100 1.126 0.1 MinMaxScaler()   0 -> 0.5781573578041792
  641/3780 0.100 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5460250932224416
  642/3780 0.100 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5488542962017521
  643/3780 0.100 1.126 1.0 StandardScaler() 0 -> 0.5595169313240529
  644/3780 0.100 1.126 1.0 MinMaxScaler()   0 -> 0.545200584008644
  645/3780 0.100 1.429 scale StandardScaler() 0 -> 0.5454024828785434
  646/3780 0.100 1.429 scale MinMaxScaler()   0 -> 0.542616768876242
  647/3780 0.100 1.429 auto StandardScaler() 0 -> 0.5454024828785426
  648/3780 0.100 1.429 auto MinMaxScaler()   0 -> 0.55542420379409
  649/3780 0.100 1.429 0.01 StandardScaler() 0 -> 0.5495704915803915
  650/3780 0.100 1.429 0.01 MinMaxScaler()   0 -> 0.6166543956267855
  651/3780 0.100 1.429 0.03162277660168379 StandardScaler() 0 -> 0.545309404581506
  652/3780 0.100 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5892989188261677
  653/3780 0.100 1.429 0.1 StandardScaler() 0 -> 0.5455099027775223
  654/3780 0.100 1.429 0.1 MinMaxScaler()   0 -> 0.5755364511282388
  655/3780 0.100 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5483097641825121
  656/3780 0.100 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5482237039716978
  657/3780 0.100 1.429 1.0 StandardScaler() 0 -> 0.5666862107036296
  658/3780 0.100 1.429 1.0 MinMaxScaler()   0 -> 0.5452266527516006
  659/3780 0.100 1.814 scale StandardScaler() 0 -> 0.5460817539808319
  660/3780 0.100 1.814 scale MinMaxScaler()   0 -> 0.5436596853441734
  661/3780 0.100 1.814 auto StandardScaler() 0 -> 0.5460817539808315
  662/3780 0.100 1.814 auto MinMaxScaler()   0 -> 0.5520047205875003
  663/3780 0.100 1.814 0.01 StandardScaler() 0 -> 0.5481682378991097
  664/3780 0.100 1.814 0.01 MinMaxScaler()   0 -> 0.6067871299150792
  665/3780 0.100 1.814 0.03162277660168379 StandardScaler() 0 -> 0.545434142893825
  666/3780 0.100 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5884926842579182
  667/3780 0.100 1.814 0.1 StandardScaler() 0 -> 0.5456746041462005
  668/3780 0.100 1.814 0.1 MinMaxScaler()   0 -> 0.5726055788472598
  669/3780 0.100 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5508132980212875
  670/3780 0.100 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5477718084605764
  671/3780 0.100 1.814 1.0 StandardScaler() 0 -> 0.5758397996180881
  672/3780 0.100 1.814 1.0 MinMaxScaler()   0 -> 0.5453345227454326
  673/3780 0.100 2.302 scale StandardScaler() 0 -> 0.5476764700242405
  674/3780 0.100 2.302 scale MinMaxScaler()   0 -> 0.5456429128001593
  675/3780 0.100 2.302 auto StandardScaler() 0 -> 0.5476764700242405
  676/3780 0.100 2.302 auto MinMaxScaler()   0 -> 0.5504313724874185
  677/3780 0.100 2.302 0.01 StandardScaler() 0 -> 0.5475162577009521
  678/3780 0.100 2.302 0.01 MinMaxScaler()   0 -> 0.5986332559058077
  679/3780 0.100 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5452916380298518
  680/3780 0.100 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5879350495466898
  681/3780 0.100 2.302 0.1 StandardScaler() 0 -> 0.5456419524611937
  682/3780 0.100 2.302 0.1 MinMaxScaler()   0 -> 0.569008348416414
  683/3780 0.100 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5549506787344937
  684/3780 0.100 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5470660184545407
  685/3780 0.100 2.302 1.0 StandardScaler() 0 -> 0.5878573578103803
  686/3780 0.100 2.302 1.0 MinMaxScaler()   0 -> 0.5456649805869935
  687/3780 0.100 2.921 scale StandardScaler() 0 -> 0.5498919404810212
  688/3780 0.100 2.921 scale MinMaxScaler()   0 -> 0.5478557348580648
  689/3780 0.100 2.921 auto StandardScaler() 0 -> 0.5498919404810199
  690/3780 0.100 2.921 auto MinMaxScaler()   0 -> 0.5489974984166227
  691/3780 0.100 2.921 0.01 StandardScaler() 0 -> 0.5473517737009496
  692/3780 0.100 2.921 0.01 MinMaxScaler()   0 -> 0.5933194715458271
  693/3780 0.100 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5461524564508713
  694/3780 0.100 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.587290927459375
  695/3780 0.100 2.921 0.1 StandardScaler() 0 -> 0.5453886774407539
  696/3780 0.100 2.921 0.1 MinMaxScaler()   0 -> 0.5654034390900103
  697/3780 0.100 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5591821096931849
  698/3780 0.100 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5467674508429569
  699/3780 0.100 2.921 1.0 StandardScaler() 0 -> 0.6012571618085657
  700/3780 0.100 2.921 1.0 MinMaxScaler()   0 -> 0.5458238811312884
  701/3780 0.100 3.707 scale StandardScaler() 0 -> 0.5522402284814611
  702/3780 0.100 3.707 scale MinMaxScaler()   0 -> 0.5502821213419677
  703/3780 0.100 3.707 auto StandardScaler() 0 -> 0.5522402284814633
  704/3780 0.100 3.707 auto MinMaxScaler()   0 -> 0.5488085769526369
  705/3780 0.100 3.707 0.01 StandardScaler() 0 -> 0.5469136428034521
  706/3780 0.100 3.707 0.01 MinMaxScaler()   0 -> 0.5915902916023928
  707/3780 0.100 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5458051581988254
  708/3780 0.100 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5862595842411797
  709/3780 0.100 3.707 0.1 StandardScaler() 0 -> 0.5455805407262398
  710/3780 0.100 3.707 0.1 MinMaxScaler()   0 -> 0.5613248956606074
  711/3780 0.100 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5639715098405383
  712/3780 0.100 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5466595308321253
  713/3780 0.100 3.707 1.0 StandardScaler() 0 -> 0.6135135293868856
  714/3780 0.100 3.707 1.0 MinMaxScaler()   0 -> 0.5457356039706989
  715/3780 0.100 4.703 scale StandardScaler() 0 -> 0.5552311486874438
  716/3780 0.100 4.703 scale MinMaxScaler()   0 -> 0.5523414958873674
  717/3780 0.100 4.703 auto StandardScaler() 0 -> 0.5552311486874423
  718/3780 0.100 4.703 auto MinMaxScaler()   0 -> 0.5482146263014468
  719/3780 0.100 4.703 0.01 StandardScaler() 0 -> 0.5470996050510588
  720/3780 0.100 4.703 0.01 MinMaxScaler()   0 -> 0.5906718516336366
  721/3780 0.100 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5463989833956425
  722/3780 0.100 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5849960752799354
  723/3780 0.100 4.703 0.1 StandardScaler() 0 -> 0.5459765244875103
  724/3780 0.100 4.703 0.1 MinMaxScaler()   0 -> 0.5575733157074431
  725/3780 0.100 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5691419801174002
  726/3780 0.100 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5463329050731636
  727/3780 0.100 4.703 1.0 StandardScaler() 0 -> 0.6276225071106581
  728/3780 0.100 4.703 1.0 MinMaxScaler()   0 -> 0.545661044545198
  729/3780 0.100 5.968 scale StandardScaler() 0 -> 0.5605855388847584
  730/3780 0.100 5.968 scale MinMaxScaler()   0 -> 0.5548633372473896
  731/3780 0.100 5.968 auto StandardScaler() 0 -> 0.5605855388847583
  732/3780 0.100 5.968 auto MinMaxScaler()   0 -> 0.547445248716263
  733/3780 0.100 5.968 0.01 StandardScaler() 0 -> 0.5469054613572712
  734/3780 0.100 5.968 0.01 MinMaxScaler()   0 -> 0.589891757516116
  735/3780 0.100 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5465211051227571
  736/3780 0.100 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.583488863881286
  737/3780 0.100 5.968 0.1 StandardScaler() 0 -> 0.5470070542131041
  738/3780 0.100 5.968 0.1 MinMaxScaler()   0 -> 0.5542544151596176
  739/3780 0.100 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5747816628483032
  740/3780 0.100 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5463469256081486
  741/3780 0.100 5.968 1.0 StandardScaler() 0 -> 0.6451774892503165
  742/3780 0.100 5.968 1.0 MinMaxScaler()   0 -> 0.5461983473720958
  743/3780 0.100 7.574 scale StandardScaler() 0 -> 0.5655027688788369
  744/3780 0.100 7.574 scale MinMaxScaler()   0 -> 0.5585772046274206
  745/3780 0.100 7.574 auto StandardScaler() 0 -> 0.5655027688788347
  746/3780 0.100 7.574 auto MinMaxScaler()   0 -> 0.5475098909875183
  747/3780 0.100 7.574 0.01 StandardScaler() 0 -> 0.5470390098329138
  748/3780 0.100 7.574 0.01 MinMaxScaler()   0 -> 0.5898260689791552
  749/3780 0.100 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5461401002891408
  750/3780 0.100 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5815260337534833
  751/3780 0.100 7.574 0.1 StandardScaler() 0 -> 0.5483894732346374
  752/3780 0.100 7.574 0.1 MinMaxScaler()   0 -> 0.5520432015967724
  753/3780 0.100 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5824940606328545
  754/3780 0.100 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5463677656291224
  755/3780 0.100 7.574 1.0 StandardScaler() 0 -> 0.6624286582584147
  756/3780 0.100 7.574 1.0 MinMaxScaler()   0 -> 0.5462851215212544
  757/3780 0.100 9.611 scale StandardScaler() 0 -> 0.5704073347081291
  758/3780 0.100 9.611 scale MinMaxScaler()   0 -> 0.5626020840824203
  759/3780 0.100 9.611 auto StandardScaler() 0 -> 0.570407334708123
  760/3780 0.100 9.611 auto MinMaxScaler()   0 -> 0.547330683423744
  761/3780 0.100 9.611 0.01 StandardScaler() 0 -> 0.5467975156117629
  762/3780 0.100 9.611 0.01 MinMaxScaler()   0 -> 0.589638712536883
  763/3780 0.100 9.611 0.03162277660168379 StandardScaler() 0 -> 0.546452001169626
  764/3780 0.100 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5795949621691264
  765/3780 0.100 9.611 0.1 StandardScaler() 0 -> 0.5497946120259205
  766/3780 0.100 9.611 0.1 MinMaxScaler()   0 -> 0.5506231956010716
  767/3780 0.100 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5914607989671988
  768/3780 0.100 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5466632828658088
  769/3780 0.100 9.611 1.0 StandardScaler() 0 -> 0.6815100731177632
  770/3780 0.100 9.611 1.0 MinMaxScaler()   0 -> 0.5470092435924543
  771/3780 0.100 12.196 scale StandardScaler() 0 -> 0.5757389423983706
  772/3780 0.100 12.196 scale MinMaxScaler()   0 -> 0.5674293972537406
  773/3780 0.100 12.196 auto StandardScaler() 0 -> 0.5757389423983695
  774/3780 0.100 12.196 auto MinMaxScaler()   0 -> 0.5471954388785244
  775/3780 0.100 12.196 0.01 StandardScaler() 0 -> 0.5470548281783273
  776/3780 0.100 12.196 0.01 MinMaxScaler()   0 -> 0.5897385883397331
  777/3780 0.100 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5467631363758544
  778/3780 0.100 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5775255831725111
  779/3780 0.100 12.196 0.1 StandardScaler() 0 -> 0.5522568433608785
  780/3780 0.100 12.196 0.1 MinMaxScaler()   0 -> 0.5494027900970687
  781/3780 0.100 12.196 0.31622776601683794 StandardScaler() 0 -> 0.6012302757635695
  782/3780 0.100 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5469401227345217
  783/3780 0.100 12.196 1.0 StandardScaler() 0 -> 0.7041682116915017
  784/3780 0.100 12.196 1.0 MinMaxScaler()   0 -> 0.5471692963278606
  785/3780 0.100 15.476 scale StandardScaler() 0 -> 0.5821478761870406
  786/3780 0.100 15.476 scale MinMaxScaler()   0 -> 0.5731722949069247
  787/3780 0.100 15.476 auto StandardScaler() 0 -> 0.5821478761870399
  788/3780 0.100 15.476 auto MinMaxScaler()   0 -> 0.5472826813462479
  789/3780 0.100 15.476 0.01 StandardScaler() 0 -> 0.5472086710818306
  790/3780 0.100 15.476 0.01 MinMaxScaler()   0 -> 0.5893678858058901
  791/3780 0.100 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5470100196690798
  792/3780 0.100 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5743885802431193
  793/3780 0.100 15.476 0.1 StandardScaler() 0 -> 0.5544512912017697
  794/3780 0.100 15.476 0.1 MinMaxScaler()   0 -> 0.549292799747051
  795/3780 0.100 15.476 0.31622776601683794 StandardScaler() 0 -> 0.6109892790597548
  796/3780 0.100 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5467424375725266
  797/3780 0.100 15.476 1.0 StandardScaler() 0 -> 0.727670303497416
  798/3780 0.100 15.476 1.0 MinMaxScaler()   0 -> 0.547264922811758
  799/3780 0.100 19.638 scale StandardScaler() 0 -> 0.5896785325155698
  800/3780 0.100 19.638 scale MinMaxScaler()   0 -> 0.5790280664088775
  801/3780 0.100 19.638 auto StandardScaler() 0 -> 0.5896785325155748
  802/3780 0.100 19.638 auto MinMaxScaler()   0 -> 0.5474624832845115
  803/3780 0.100 19.638 0.01 StandardScaler() 0 -> 0.5472202422942507
  804/3780 0.100 19.638 0.01 MinMaxScaler()   0 -> 0.58895244816713
  805/3780 0.100 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5474353968163357
  806/3780 0.100 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5710553882305006
  807/3780 0.100 19.638 0.1 StandardScaler() 0 -> 0.5565782824541792
  808/3780 0.100 19.638 0.1 MinMaxScaler()   0 -> 0.5486027073665868
  809/3780 0.100 19.638 0.31622776601683794 StandardScaler() 0 -> 0.6217757801540259
  810/3780 0.100 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5463830424223305
  811/3780 0.100 19.638 1.0 StandardScaler() 0 -> 0.7553547499814574
  812/3780 0.100 19.638 1.0 MinMaxScaler()   0 -> 0.5473288628437426
  813/3780 0.100 24.920 scale StandardScaler() 0 -> 0.5984667480108459
  814/3780 0.100 24.920 scale MinMaxScaler()   0 -> 0.5845431832254815
  815/3780 0.100 24.920 auto StandardScaler() 0 -> 0.5984728678305883
  816/3780 0.100 24.920 auto MinMaxScaler()   0 -> 0.5473043951303377
  817/3780 0.100 24.920 0.01 StandardScaler() 0 -> 0.5473352127868311
  818/3780 0.100 24.920 0.01 MinMaxScaler()   0 -> 0.5881253282076252
  819/3780 0.100 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5473301045242777
  820/3780 0.100 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5673175855175855
  821/3780 0.100 24.920 0.1 StandardScaler() 0 -> 0.5595331292179259
  822/3780 0.100 24.920 0.1 MinMaxScaler()   0 -> 0.5481043992442493
  823/3780 0.100 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6351330830618999
  824/3780 0.100 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.547012172343086
  825/3780 0.100 24.920 1.0 StandardScaler() 0 -> 0.7890694872386846
  826/3780 0.100 24.920 1.0 MinMaxScaler()   0 -> 0.5473399436184256
  827/3780 0.100 31.623 scale StandardScaler() 0 -> 0.6074196874494875
  828/3780 0.100 31.623 scale MinMaxScaler()   0 -> 0.5900229288015783
  829/3780 0.100 31.623 auto StandardScaler() 0 -> 0.6074196874494898
  830/3780 0.100 31.623 auto MinMaxScaler()   0 -> 0.547651731719682
  831/3780 0.100 31.623 0.01 StandardScaler() 0 -> 0.5473305921635695
  832/3780 0.100 31.623 0.01 MinMaxScaler()   0 -> 0.5872208931269495
  833/3780 0.100 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5479683232127665
  834/3780 0.100 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5635744254325002
  835/3780 0.100 31.623 0.1 StandardScaler() 0 -> 0.5619847363124661
  836/3780 0.100 31.623 0.1 MinMaxScaler()   0 -> 0.5482993621872168
  837/3780 0.100 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6500447736453102
  838/3780 0.100 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5473281273494983
  839/3780 0.100 31.623 1.0 StandardScaler() 0 -> 0.8247209026733358
  840/3780 0.100 31.623 1.0 MinMaxScaler()   0 -> 0.5478095705736301
  841/3780 0.150 0.032 scale StandardScaler() 0 -> 0.5658350539967549
  842/3780 0.150 0.032 scale MinMaxScaler()   0 -> 0.5614297613102163
  843/3780 0.150 0.032 auto StandardScaler() 0 -> 0.5658350539967548
  844/3780 0.150 0.032 auto MinMaxScaler()   0 -> 0.6387008314560504
  845/3780 0.150 0.032 0.01 StandardScaler() 0 -> 0.6346815398309668
  846/3780 0.150 0.032 0.01 MinMaxScaler()   0 -> 0.7450137117296002
  847/3780 0.150 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5880937290115078
  848/3780 0.150 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7161899401198418
  849/3780 0.150 0.032 0.1 StandardScaler() 0 -> 0.5632381486054289
  850/3780 0.150 0.032 0.1 MinMaxScaler()   0 -> 0.6652842279751056
  851/3780 0.150 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5741917264224462
  852/3780 0.150 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6202297230815496
  853/3780 0.150 0.032 1.0 StandardScaler() 0 -> 0.6271886781128054
  854/3780 0.150 0.032 1.0 MinMaxScaler()   0 -> 0.5792437479009611
  855/3780 0.150 0.040 scale StandardScaler() 0 -> 0.5599901077821382
  856/3780 0.150 0.040 scale MinMaxScaler()   0 -> 0.5561129005856704
  857/3780 0.150 0.040 auto StandardScaler() 0 -> 0.5599901077821382
  858/3780 0.150 0.040 auto MinMaxScaler()   0 -> 0.6288730346220919
  859/3780 0.150 0.040 0.01 StandardScaler() 0 -> 0.6239533176435481
  860/3780 0.150 0.040 0.01 MinMaxScaler()   0 -> 0.7411459641022625
  861/3780 0.150 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5809881404100622
  862/3780 0.150 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7059959045213241
  863/3780 0.150 0.040 0.1 StandardScaler() 0 -> 0.5566988391434787
  864/3780 0.150 0.040 0.1 MinMaxScaler()   0 -> 0.6566554630296622
  865/3780 0.150 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5674903028332285
  866/3780 0.150 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.6096021605932114
  867/3780 0.150 0.040 1.0 StandardScaler() 0 -> 0.6145773194510166
  868/3780 0.150 0.040 1.0 MinMaxScaler()   0 -> 0.5724833981486132
  869/3780 0.150 0.051 scale StandardScaler() 0 -> 0.5551114409153172
  870/3780 0.150 0.051 scale MinMaxScaler()   0 -> 0.5530154070510248
  871/3780 0.150 0.051 auto StandardScaler() 0 -> 0.555111440915317
  872/3780 0.150 0.051 auto MinMaxScaler()   0 -> 0.618220779679585
  873/3780 0.150 0.051 0.01 StandardScaler() 0 -> 0.6139028970714463
  874/3780 0.150 0.051 0.01 MinMaxScaler()   0 -> 0.7363366615871426
  875/3780 0.150 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5742250323053748
  876/3780 0.150 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6941099981375
  877/3780 0.150 0.051 0.1 StandardScaler() 0 -> 0.5529416912336566
  878/3780 0.150 0.051 0.1 MinMaxScaler()   0 -> 0.6462231351069844
  879/3780 0.150 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5626872081699591
  880/3780 0.150 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.6005522531371983
  881/3780 0.150 0.051 1.0 StandardScaler() 0 -> 0.6030180798897542
  882/3780 0.150 0.051 1.0 MinMaxScaler()   0 -> 0.5659303435179383
  883/3780 0.150 0.065 scale StandardScaler() 0 -> 0.5523421154510068
  884/3780 0.150 0.065 scale MinMaxScaler()   0 -> 0.5503195571900709
  885/3780 0.150 0.065 auto StandardScaler() 0 -> 0.5523421154510068
  886/3780 0.150 0.065 auto MinMaxScaler()   0 -> 0.6081422749904166
  887/3780 0.150 0.065 0.01 StandardScaler() 0 -> 0.6046171880707517
  888/3780 0.150 0.065 0.01 MinMaxScaler()   0 -> 0.7303928067244053
  889/3780 0.150 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5674646131031349
  890/3780 0.150 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6814859660221048
  891/3780 0.150 0.065 0.1 StandardScaler() 0 -> 0.54979900892629
  892/3780 0.150 0.065 0.1 MinMaxScaler()   0 -> 0.6365452428352106
  893/3780 0.150 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5581217693990571
  894/3780 0.150 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5930286632221756
  895/3780 0.150 0.065 1.0 StandardScaler() 0 -> 0.5924341882793133
  896/3780 0.150 0.065 1.0 MinMaxScaler()   0 -> 0.5601906168566643
  897/3780 0.150 0.082 scale StandardScaler() 0 -> 0.5498105092406095
  898/3780 0.150 0.082 scale MinMaxScaler()   0 -> 0.5480339457302563
  899/3780 0.150 0.082 auto StandardScaler() 0 -> 0.5498105092406094
  900/3780 0.150 0.082 auto MinMaxScaler()   0 -> 0.6000239092663097
  901/3780 0.150 0.082 0.01 StandardScaler() 0 -> 0.5959562049093634
  902/3780 0.150 0.082 0.01 MinMaxScaler()   0 -> 0.7231062579426905
  903/3780 0.150 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5617409158406912
  904/3780 0.150 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.671552826734437
  905/3780 0.150 0.082 0.1 StandardScaler() 0 -> 0.5478377006720646
  906/3780 0.150 0.082 0.1 MinMaxScaler()   0 -> 0.6268148893723605
  907/3780 0.150 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5537567493491206
  908/3780 0.150 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.587195590745366
  909/3780 0.150 0.082 1.0 StandardScaler() 0 -> 0.5821712429820688
  910/3780 0.150 0.082 1.0 MinMaxScaler()   0 -> 0.5560352568487622
  911/3780 0.150 0.104 scale StandardScaler() 0 -> 0.5477711910085215
  912/3780 0.150 0.104 scale MinMaxScaler()   0 -> 0.5463177161548763
  913/3780 0.150 0.104 auto StandardScaler() 0 -> 0.5477711910085213
  914/3780 0.150 0.104 auto MinMaxScaler()   0 -> 0.5933983871131504
  915/3780 0.150 0.104 0.01 StandardScaler() 0 -> 0.5897391092419507
  916/3780 0.150 0.104 0.01 MinMaxScaler()   0 -> 0.7142706514508838
  917/3780 0.150 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5568685846697482
  918/3780 0.150 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6628153148195279
  919/3780 0.150 0.104 0.1 StandardScaler() 0 -> 0.5458589952291998
  920/3780 0.150 0.104 0.1 MinMaxScaler()   0 -> 0.616443603510594
  921/3780 0.150 0.104 0.31622776601683794 StandardScaler() 0 -> 0.550769224387246
  922/3780 0.150 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5830055083654617
  923/3780 0.150 0.104 1.0 StandardScaler() 0 -> 0.5729204678593119
  924/3780 0.150 0.104 1.0 MinMaxScaler()   0 -> 0.5518409956123341
  925/3780 0.150 0.132 scale StandardScaler() 0 -> 0.5458057511336659
  926/3780 0.150 0.132 scale MinMaxScaler()   0 -> 0.5446494100731445
  927/3780 0.150 0.132 auto StandardScaler() 0 -> 0.5458057511336657
  928/3780 0.150 0.132 auto MinMaxScaler()   0 -> 0.5879435276711761
  929/3780 0.150 0.132 0.01 StandardScaler() 0 -> 0.5850881862345707
  930/3780 0.150 0.132 0.01 MinMaxScaler()   0 -> 0.703722847094881
  931/3780 0.150 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5527945744865924
  932/3780 0.150 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6541602305487936
  933/3780 0.150 0.132 0.1 StandardScaler() 0 -> 0.544824278578455
  934/3780 0.150 0.132 0.1 MinMaxScaler()   0 -> 0.6070153547179095
  935/3780 0.150 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5480631723276174
  936/3780 0.150 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5788272640135131
  937/3780 0.150 0.132 1.0 StandardScaler() 0 -> 0.5646367054712736
  938/3780 0.150 0.132 1.0 MinMaxScaler()   0 -> 0.549741790652414
  939/3780 0.150 0.168 scale StandardScaler() 0 -> 0.5442528148052862
  940/3780 0.150 0.168 scale MinMaxScaler()   0 -> 0.5432753363293106
  941/3780 0.150 0.168 auto StandardScaler() 0 -> 0.5442528148052861
  942/3780 0.150 0.168 auto MinMaxScaler()   0 -> 0.5849410374974382
  943/3780 0.150 0.168 0.01 StandardScaler() 0 -> 0.581370362995735
  944/3780 0.150 0.168 0.01 MinMaxScaler()   0 -> 0.6915805557903223
  945/3780 0.150 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5505153125067274
  946/3780 0.150 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6440292710703542
  947/3780 0.150 0.168 0.1 StandardScaler() 0 -> 0.5440934166316419
  948/3780 0.150 0.168 0.1 MinMaxScaler()   0 -> 0.599863267192161
  949/3780 0.150 0.168 0.31622776601683794 StandardScaler() 0 -> 0.545579755988888
  950/3780 0.150 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5751015249680825
  951/3780 0.150 0.168 1.0 StandardScaler() 0 -> 0.5577015564107065
  952/3780 0.150 0.168 1.0 MinMaxScaler()   0 -> 0.5476782874928455
  953/3780 0.150 0.213 scale StandardScaler() 0 -> 0.5432642172927477
  954/3780 0.150 0.213 scale MinMaxScaler()   0 -> 0.5419120154261999
  955/3780 0.150 0.213 auto StandardScaler() 0 -> 0.5432642172868415
  956/3780 0.150 0.213 auto MinMaxScaler()   0 -> 0.5818018207485833
  957/3780 0.150 0.213 0.01 StandardScaler() 0 -> 0.5771095303168376
  958/3780 0.150 0.213 0.01 MinMaxScaler()   0 -> 0.6791747699782661
  959/3780 0.150 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5488877355114342
  960/3780 0.150 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6344519801742082
  961/3780 0.150 0.213 0.1 StandardScaler() 0 -> 0.5433117987984646
  962/3780 0.150 0.213 0.1 MinMaxScaler()   0 -> 0.5937805224977203
  963/3780 0.150 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5437963529531998
  964/3780 0.150 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5710220518686856
  965/3780 0.150 0.213 1.0 StandardScaler() 0 -> 0.5519182911671421
  966/3780 0.150 0.213 1.0 MinMaxScaler()   0 -> 0.5461515093656487
  967/3780 0.150 0.270 scale StandardScaler() 0 -> 0.5422223005876946
  968/3780 0.150 0.270 scale MinMaxScaler()   0 -> 0.5402436257679663
  969/3780 0.150 0.270 auto StandardScaler() 0 -> 0.5422223005681089
  970/3780 0.150 0.270 auto MinMaxScaler()   0 -> 0.5789560910362678
  971/3780 0.150 0.270 0.01 StandardScaler() 0 -> 0.5732898105503895
  972/3780 0.150 0.270 0.01 MinMaxScaler()   0 -> 0.669699596179954
  973/3780 0.150 0.270 0.03162277660168379 StandardScaler() 0 -> 0.54702297072465
  974/3780 0.150 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6243209958300534
  975/3780 0.150 0.270 0.1 StandardScaler() 0 -> 0.5430815853066252
  976/3780 0.150 0.270 0.1 MinMaxScaler()   0 -> 0.5894774101338613
  977/3780 0.150 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5419139624658477
  978/3780 0.150 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.567077581324162
  979/3780 0.150 0.270 1.0 StandardScaler() 0 -> 0.5478692053433054
  980/3780 0.150 0.270 1.0 MinMaxScaler()   0 -> 0.5450439286292235
  981/3780 0.150 0.342 scale StandardScaler() 0 -> 0.5414645305080501
  982/3780 0.150 0.342 scale MinMaxScaler()   0 -> 0.5390349537434017
  983/3780 0.150 0.342 auto StandardScaler() 0 -> 0.5414645305080498
  984/3780 0.150 0.342 auto MinMaxScaler()   0 -> 0.5757453342140807
  985/3780 0.150 0.342 0.01 StandardScaler() 0 -> 0.5689940264298826
  986/3780 0.150 0.342 0.01 MinMaxScaler()   0 -> 0.6610521092522011
  987/3780 0.150 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5454755897774854
  988/3780 0.150 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.6143490340189763
  989/3780 0.150 0.342 0.1 StandardScaler() 0 -> 0.5429782165295246
  990/3780 0.150 0.342 0.1 MinMaxScaler()   0 -> 0.5871774984919219
  991/3780 0.150 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5403179376117225
  992/3780 0.150 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5633029392376218
  993/3780 0.150 0.342 1.0 StandardScaler() 0 -> 0.545255320693793
  994/3780 0.150 0.342 1.0 MinMaxScaler()   0 -> 0.5444306098169972
  995/3780 0.150 0.434 scale StandardScaler() 0 -> 0.5399380133648042
  996/3780 0.150 0.434 scale MinMaxScaler()   0 -> 0.5381220773931958
  997/3780 0.150 0.434 auto StandardScaler() 0 -> 0.5399380133648043
  998/3780 0.150 0.434 auto MinMaxScaler()   0 -> 0.5727717407623681
  999/3780 0.150 0.434 0.01 StandardScaler() 0 -> 0.5646768854894842
 1000/3780 0.150 0.434 0.01 MinMaxScaler()   0 -> 0.6522420846665611
 1001/3780 0.150 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5449062284069229
 1002/3780 0.150 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.6059065757892106
 1003/3780 0.150 0.434 0.1 StandardScaler() 0 -> 0.5426517723984053
 1004/3780 0.150 0.434 0.1 MinMaxScaler()   0 -> 0.5852751885948347
 1005/3780 0.150 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5395004245911061
 1006/3780 0.150 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5590849556824099
 1007/3780 0.150 0.434 1.0 StandardScaler() 0 -> 0.5440745711863845
 1008/3780 0.150 0.434 1.0 MinMaxScaler()   0 -> 0.544089338504974
 1009/3780 0.150 0.551 scale StandardScaler() 0 -> 0.5396744405942735
 1010/3780 0.150 0.551 scale MinMaxScaler()   0 -> 0.5370137072135527
 1011/3780 0.150 0.551 auto StandardScaler() 0 -> 0.5396744405942732
 1012/3780 0.150 0.551 auto MinMaxScaler()   0 -> 0.5694520080852027
 1013/3780 0.150 0.551 0.01 StandardScaler() 0 -> 0.5600043044191705
 1014/3780 0.150 0.551 0.01 MinMaxScaler()   0 -> 0.6421655699600538
 1015/3780 0.150 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5443485524764005
 1016/3780 0.150 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5994834946657769
 1017/3780 0.150 0.551 0.1 StandardScaler() 0 -> 0.5418510476135224
 1018/3780 0.150 0.551 0.1 MinMaxScaler()   0 -> 0.5832539146138312
 1019/3780 0.150 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5391669623822333
 1020/3780 0.150 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5556361212524182
 1021/3780 0.150 0.551 1.0 StandardScaler() 0 -> 0.5447357338707653
 1022/3780 0.150 0.551 1.0 MinMaxScaler()   0 -> 0.5436635615118158
 1023/3780 0.150 0.700 scale StandardScaler() 0 -> 0.5399532484022863
 1024/3780 0.150 0.700 scale MinMaxScaler()   0 -> 0.5368030087792234
 1025/3780 0.150 0.700 auto StandardScaler() 0 -> 0.5399532484022859
 1026/3780 0.150 0.700 auto MinMaxScaler()   0 -> 0.5654783886238026
 1027/3780 0.150 0.700 0.01 StandardScaler() 0 -> 0.5558523734433644
 1028/3780 0.150 0.700 0.01 MinMaxScaler()   0 -> 0.6323286408399797
 1029/3780 0.150 0.700 0.03162277660168379 StandardScaler() 0 -> 0.543733362411575
 1030/3780 0.150 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5940132892631271
 1031/3780 0.150 0.700 0.1 StandardScaler() 0 -> 0.5414150438717039
 1032/3780 0.150 0.700 0.1 MinMaxScaler()   0 -> 0.5812340373269264
 1033/3780 0.150 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5395214415359214
 1034/3780 0.150 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5525846091275005
 1035/3780 0.150 0.700 1.0 StandardScaler() 0 -> 0.5465872191695301
 1036/3780 0.150 0.700 1.0 MinMaxScaler()   0 -> 0.5432767224459386
 1037/3780 0.150 0.888 scale StandardScaler() 0 -> 0.540293117207243
 1038/3780 0.150 0.888 scale MinMaxScaler()   0 -> 0.5371500785553044
 1039/3780 0.150 0.888 auto StandardScaler() 0 -> 0.540293117207243
 1040/3780 0.150 0.888 auto MinMaxScaler()   0 -> 0.5618505257198357
 1041/3780 0.150 0.888 0.01 StandardScaler() 0 -> 0.5535316690030933
 1042/3780 0.150 0.888 0.01 MinMaxScaler()   0 -> 0.6225138991140368
 1043/3780 0.150 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5434637753810594
 1044/3780 0.150 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5908370062766696
 1045/3780 0.150 0.888 0.1 StandardScaler() 0 -> 0.5404442994609266
 1046/3780 0.150 0.888 0.1 MinMaxScaler()   0 -> 0.5792186280231195
 1047/3780 0.150 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5402400576682228
 1048/3780 0.150 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5506366674501221
 1049/3780 0.150 0.888 1.0 StandardScaler() 0 -> 0.5516743014085134
 1050/3780 0.150 0.888 1.0 MinMaxScaler()   0 -> 0.5428132169242803
 1051/3780 0.150 1.126 scale StandardScaler() 0 -> 0.5404351251516064
 1052/3780 0.150 1.126 scale MinMaxScaler()   0 -> 0.5380321898979061
 1053/3780 0.150 1.126 auto StandardScaler() 0 -> 0.540435125151606
 1054/3780 0.150 1.126 auto MinMaxScaler()   0 -> 0.5582674196791714
 1055/3780 0.150 1.126 0.01 StandardScaler() 0 -> 0.5517596459250464
 1056/3780 0.150 1.126 0.01 MinMaxScaler()   0 -> 0.6129751710201176
 1057/3780 0.150 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5435305622781164
 1058/3780 0.150 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5892631693308515
 1059/3780 0.150 1.126 0.1 StandardScaler() 0 -> 0.5404111370552892
 1060/3780 0.150 1.126 0.1 MinMaxScaler()   0 -> 0.5772140741614264
 1061/3780 0.150 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5419750670396358
 1062/3780 0.150 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.54843039217418
 1063/3780 0.150 1.126 1.0 StandardScaler() 0 -> 0.5565545499745251
 1064/3780 0.150 1.126 1.0 MinMaxScaler()   0 -> 0.5425480260511124
 1065/3780 0.150 1.429 scale StandardScaler() 0 -> 0.5415700308264907
 1066/3780 0.150 1.429 scale MinMaxScaler()   0 -> 0.5385692952578227
 1067/3780 0.150 1.429 auto StandardScaler() 0 -> 0.5415700308264904
 1068/3780 0.150 1.429 auto MinMaxScaler()   0 -> 0.5546261931343454
 1069/3780 0.150 1.429 0.01 StandardScaler() 0 -> 0.5495356740188101
 1070/3780 0.150 1.429 0.01 MinMaxScaler()   0 -> 0.604816549600827
 1071/3780 0.150 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5435772947291578
 1072/3780 0.150 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5881063786775794
 1073/3780 0.150 1.429 0.1 StandardScaler() 0 -> 0.540606479218318
 1074/3780 0.150 1.429 0.1 MinMaxScaler()   0 -> 0.5744815069569938
 1075/3780 0.150 1.429 0.31622776601683794 StandardScaler() 0 -> 0.543847777517172
 1076/3780 0.150 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.547271361821481
 1077/3780 0.150 1.429 1.0 StandardScaler() 0 -> 0.5630980687427231
 1078/3780 0.150 1.429 1.0 MinMaxScaler()   0 -> 0.5428538166560698
 1079/3780 0.150 1.814 scale StandardScaler() 0 -> 0.5430131810984328
 1080/3780 0.150 1.814 scale MinMaxScaler()   0 -> 0.5401426600068141
 1081/3780 0.150 1.814 auto StandardScaler() 0 -> 0.5430131810984334
 1082/3780 0.150 1.814 auto MinMaxScaler()   0 -> 0.5519181885332466
 1083/3780 0.150 1.814 0.01 StandardScaler() 0 -> 0.5480678532661006
 1084/3780 0.150 1.814 0.01 MinMaxScaler()   0 -> 0.5986578223409309
 1085/3780 0.150 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5431192728696161
 1086/3780 0.150 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5873000393608115
 1087/3780 0.150 1.814 0.1 StandardScaler() 0 -> 0.5408195999429442
 1088/3780 0.150 1.814 0.1 MinMaxScaler()   0 -> 0.5715459100792127
 1089/3780 0.150 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5476399018975538
 1090/3780 0.150 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5461310615614168
 1091/3780 0.150 1.814 1.0 StandardScaler() 0 -> 0.5723285496867151
 1092/3780 0.150 1.814 1.0 MinMaxScaler()   0 -> 0.5428267870914881
 1093/3780 0.150 2.302 scale StandardScaler() 0 -> 0.544601387285731
 1094/3780 0.150 2.302 scale MinMaxScaler()   0 -> 0.5421902263202022
 1095/3780 0.150 2.302 auto StandardScaler() 0 -> 0.5446013872857304
 1096/3780 0.150 2.302 auto MinMaxScaler()   0 -> 0.5506335778750261
 1097/3780 0.150 2.302 0.01 StandardScaler() 0 -> 0.5468812459463201
 1098/3780 0.150 2.302 0.01 MinMaxScaler()   0 -> 0.5937521737248191
 1099/3780 0.150 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5434236622263896
 1100/3780 0.150 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5863556443307987
 1101/3780 0.150 2.302 0.1 StandardScaler() 0 -> 0.5414609956658817
 1102/3780 0.150 2.302 0.1 MinMaxScaler()   0 -> 0.5683778422571527
 1103/3780 0.150 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5514446525664667
 1104/3780 0.150 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5455869052106976
 1105/3780 0.150 2.302 1.0 StandardScaler() 0 -> 0.5827548527625844
 1106/3780 0.150 2.302 1.0 MinMaxScaler()   0 -> 0.5430275900030507
 1107/3780 0.150 2.921 scale StandardScaler() 0 -> 0.5463079804418302
 1108/3780 0.150 2.921 scale MinMaxScaler()   0 -> 0.5442597792768996
 1109/3780 0.150 2.921 auto StandardScaler() 0 -> 0.5463079804418288
 1110/3780 0.150 2.921 auto MinMaxScaler()   0 -> 0.5487033777017724
 1111/3780 0.150 2.921 0.01 StandardScaler() 0 -> 0.5459906251690841
 1112/3780 0.150 2.921 0.01 MinMaxScaler()   0 -> 0.5912835732361686
 1113/3780 0.150 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5430956838127691
 1114/3780 0.150 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5856120302234077
 1115/3780 0.150 2.921 0.1 StandardScaler() 0 -> 0.5413147885779378
 1116/3780 0.150 2.921 0.1 MinMaxScaler()   0 -> 0.5643792226906027
 1117/3780 0.150 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5555947740161679
 1118/3780 0.150 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5452835808971037
 1119/3780 0.150 2.921 1.0 StandardScaler() 0 -> 0.5936107849326554
 1120/3780 0.150 2.921 1.0 MinMaxScaler()   0 -> 0.5429980768250897
 1121/3780 0.150 3.707 scale StandardScaler() 0 -> 0.5487296263090379
 1122/3780 0.150 3.707 scale MinMaxScaler()   0 -> 0.5461627460267028
 1123/3780 0.150 3.707 auto StandardScaler() 0 -> 0.5487296263090391
 1124/3780 0.150 3.707 auto MinMaxScaler()   0 -> 0.5476220729478314
 1125/3780 0.150 3.707 0.01 StandardScaler() 0 -> 0.5455759255230561
 1126/3780 0.150 3.707 0.01 MinMaxScaler()   0 -> 0.5897798186447388
 1127/3780 0.150 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5430570794760404
 1128/3780 0.150 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5849754194196185
 1129/3780 0.150 3.707 0.1 StandardScaler() 0 -> 0.5414999469351375
 1130/3780 0.150 3.707 0.1 MinMaxScaler()   0 -> 0.5611837562319485
 1131/3780 0.150 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5604589054914109
 1132/3780 0.150 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5454279777569835
 1133/3780 0.150 3.707 1.0 StandardScaler() 0 -> 0.6048179350747599
 1134/3780 0.150 3.707 1.0 MinMaxScaler()   0 -> 0.5432017955433244
 1135/3780 0.150 4.703 scale StandardScaler() 0 -> 0.5517659870148878
 1136/3780 0.150 4.703 scale MinMaxScaler()   0 -> 0.5489222237471606
 1137/3780 0.150 4.703 auto StandardScaler() 0 -> 0.5517659870148862
 1138/3780 0.150 4.703 auto MinMaxScaler()   0 -> 0.5465269323685965
 1139/3780 0.150 4.703 0.01 StandardScaler() 0 -> 0.5455058491139774
 1140/3780 0.150 4.703 0.01 MinMaxScaler()   0 -> 0.5891388964839361
 1141/3780 0.150 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5434357108423136
 1142/3780 0.150 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5839900365027275
 1143/3780 0.150 4.703 0.1 StandardScaler() 0 -> 0.5422814479305463
 1144/3780 0.150 4.703 0.1 MinMaxScaler()   0 -> 0.557064236420846
 1145/3780 0.150 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5661688896069631
 1146/3780 0.150 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5450354081191325
 1147/3780 0.150 4.703 1.0 StandardScaler() 0 -> 0.6173329948008459
 1148/3780 0.150 4.703 1.0 MinMaxScaler()   0 -> 0.5433500555441593
 1149/3780 0.150 5.968 scale StandardScaler() 0 -> 0.5565878270851349
 1150/3780 0.150 5.968 scale MinMaxScaler()   0 -> 0.5525824300020657
 1151/3780 0.150 5.968 auto StandardScaler() 0 -> 0.5565878270851349
 1152/3780 0.150 5.968 auto MinMaxScaler()   0 -> 0.5460417765563627
 1153/3780 0.150 5.968 0.01 StandardScaler() 0 -> 0.5453819972743891
 1154/3780 0.150 5.968 0.01 MinMaxScaler()   0 -> 0.5883988259932716
 1155/3780 0.150 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5429360414438881
 1156/3780 0.150 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5826122215221603
 1157/3780 0.150 5.968 0.1 StandardScaler() 0 -> 0.5443538445843802
 1158/3780 0.150 5.968 0.1 MinMaxScaler()   0 -> 0.554049944161118
 1159/3780 0.150 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5732360248041263
 1160/3780 0.150 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5446984272997298
 1161/3780 0.150 5.968 1.0 StandardScaler() 0 -> 0.6315447365734984
 1162/3780 0.150 5.968 1.0 MinMaxScaler()   0 -> 0.5430844613521382
 1163/3780 0.150 7.574 scale StandardScaler() 0 -> 0.5607964746213111
 1164/3780 0.150 7.574 scale MinMaxScaler()   0 -> 0.5555298225570994
 1165/3780 0.150 7.574 auto StandardScaler() 0 -> 0.5607964746213092
 1166/3780 0.150 7.574 auto MinMaxScaler()   0 -> 0.5459747697152438
 1167/3780 0.150 7.574 0.01 StandardScaler() 0 -> 0.5453181134740285
 1168/3780 0.150 7.574 0.01 MinMaxScaler()   0 -> 0.5880914827526353
 1169/3780 0.150 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5428094308098039
 1170/3780 0.150 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5810283403907207
 1171/3780 0.150 7.574 0.1 StandardScaler() 0 -> 0.5460723237122989
 1172/3780 0.150 7.574 0.1 MinMaxScaler()   0 -> 0.5519208285828943
 1173/3780 0.150 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5808259001096431
 1174/3780 0.150 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5444153404824049
 1175/3780 0.150 7.574 1.0 StandardScaler() 0 -> 0.6472368547049606
 1176/3780 0.150 7.574 1.0 MinMaxScaler()   0 -> 0.542847820049379
 1177/3780 0.150 9.611 scale StandardScaler() 0 -> 0.5653832120132446
 1178/3780 0.150 9.611 scale MinMaxScaler()   0 -> 0.5596285434740276
 1179/3780 0.150 9.611 auto StandardScaler() 0 -> 0.5653832120132357
 1180/3780 0.150 9.611 auto MinMaxScaler()   0 -> 0.5459614929009013
 1181/3780 0.150 9.611 0.01 StandardScaler() 0 -> 0.5452097280059566
 1182/3780 0.150 9.611 0.01 MinMaxScaler()   0 -> 0.5879578029775927
 1183/3780 0.150 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5425814117588367
 1184/3780 0.150 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5790438684837977
 1185/3780 0.150 9.611 0.1 StandardScaler() 0 -> 0.5470374167447974
 1186/3780 0.150 9.611 0.1 MinMaxScaler()   0 -> 0.5502572905320439
 1187/3780 0.150 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5884549806586958
 1188/3780 0.150 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5444168621294996
 1189/3780 0.150 9.611 1.0 StandardScaler() 0 -> 0.665632763329071
 1190/3780 0.150 9.611 1.0 MinMaxScaler()   0 -> 0.542851858211795
 1191/3780 0.150 12.196 scale StandardScaler() 0 -> 0.5712761791032722
 1192/3780 0.150 12.196 scale MinMaxScaler()   0 -> 0.563985121277515
 1193/3780 0.150 12.196 auto StandardScaler() 0 -> 0.5712761791032741
 1194/3780 0.150 12.196 auto MinMaxScaler()   0 -> 0.5456392776225415
 1195/3780 0.150 12.196 0.01 StandardScaler() 0 -> 0.5452104319946088
 1196/3780 0.150 12.196 0.01 MinMaxScaler()   0 -> 0.5879479413274277
 1197/3780 0.150 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5425275342646417
 1198/3780 0.150 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5767303228589323
 1199/3780 0.150 12.196 0.1 StandardScaler() 0 -> 0.5490670722515406
 1200/3780 0.150 12.196 0.1 MinMaxScaler()   0 -> 0.548856288467099
 1201/3780 0.150 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5964266076591214
 1202/3780 0.150 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5441659921270072
 1203/3780 0.150 12.196 1.0 StandardScaler() 0 -> 0.6865294679654589
 1204/3780 0.150 12.196 1.0 MinMaxScaler()   0 -> 0.5430990367987009
 1205/3780 0.150 15.476 scale StandardScaler() 0 -> 0.5770076231493045
 1206/3780 0.150 15.476 scale MinMaxScaler()   0 -> 0.5688231269167237
 1207/3780 0.150 15.476 auto StandardScaler() 0 -> 0.5770076231493045
 1208/3780 0.150 15.476 auto MinMaxScaler()   0 -> 0.5454474973436008
 1209/3780 0.150 15.476 0.01 StandardScaler() 0 -> 0.5452794542436877
 1210/3780 0.150 15.476 0.01 MinMaxScaler()   0 -> 0.5878516212078676
 1211/3780 0.150 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5425546600837532
 1212/3780 0.150 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5740861013714024
 1213/3780 0.150 15.476 0.1 StandardScaler() 0 -> 0.5511633977018993
 1214/3780 0.150 15.476 0.1 MinMaxScaler()   0 -> 0.5480963202065127
 1215/3780 0.150 15.476 0.31622776601683794 StandardScaler() 0 -> 0.6057549930767403
 1216/3780 0.150 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5442758221139786
 1217/3780 0.150 15.476 1.0 StandardScaler() 0 -> 0.7087694886128629
 1218/3780 0.150 15.476 1.0 MinMaxScaler()   0 -> 0.5426036748766984
 1219/3780 0.150 19.638 scale StandardScaler() 0 -> 0.5850629239073402
 1220/3780 0.150 19.638 scale MinMaxScaler()   0 -> 0.5734081219634012
 1221/3780 0.150 19.638 auto StandardScaler() 0 -> 0.5850629239073447
 1222/3780 0.150 19.638 auto MinMaxScaler()   0 -> 0.5453444289927617
 1223/3780 0.150 19.638 0.01 StandardScaler() 0 -> 0.5449941750386127
 1224/3780 0.150 19.638 0.01 MinMaxScaler()   0 -> 0.587515811681921
 1225/3780 0.150 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5426562660782405
 1226/3780 0.150 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5708079920568184
 1227/3780 0.150 19.638 0.1 StandardScaler() 0 -> 0.5531938776478849
 1228/3780 0.150 19.638 0.1 MinMaxScaler()   0 -> 0.5468893364701972
 1229/3780 0.150 19.638 0.31622776601683794 StandardScaler() 0 -> 0.6163579010668264
 1230/3780 0.150 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.544115350293739
 1231/3780 0.150 19.638 1.0 StandardScaler() 0 -> 0.7357299692998037
 1232/3780 0.150 19.638 1.0 MinMaxScaler()   0 -> 0.5421813066229029
 1233/3780 0.150 24.920 scale StandardScaler() 0 -> 0.5929445583197017
 1234/3780 0.150 24.920 scale MinMaxScaler()   0 -> 0.5787832200260502
 1235/3780 0.150 24.920 auto StandardScaler() 0 -> 0.5929445583196996
 1236/3780 0.150 24.920 auto MinMaxScaler()   0 -> 0.545260301727177
 1237/3780 0.150 24.920 0.01 StandardScaler() 0 -> 0.5445541100976526
 1238/3780 0.150 24.920 0.01 MinMaxScaler()   0 -> 0.5871783441409237
 1239/3780 0.150 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5424971644701143
 1240/3780 0.150 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5671494679363714
 1241/3780 0.150 24.920 0.1 StandardScaler() 0 -> 0.5559591994939671
 1242/3780 0.150 24.920 0.1 MinMaxScaler()   0 -> 0.5467412501027232
 1243/3780 0.150 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6297291312470642
 1244/3780 0.150 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5439000612094361
 1245/3780 0.150 24.920 1.0 StandardScaler() 0 -> 0.7669490024330919
 1246/3780 0.150 24.920 1.0 MinMaxScaler()   0 -> 0.5423445560649349
 1247/3780 0.150 31.623 scale StandardScaler() 0 -> 0.6039126463386152
 1248/3780 0.150 31.623 scale MinMaxScaler()   0 -> 0.5833850353725455
 1249/3780 0.150 31.623 auto StandardScaler() 0 -> 0.603912646338627
 1250/3780 0.150 31.623 auto MinMaxScaler()   0 -> 0.5452100962191334
 1251/3780 0.150 31.623 0.01 StandardScaler() 0 -> 0.5444404236643424
 1252/3780 0.150 31.623 0.01 MinMaxScaler()   0 -> 0.586338718079864
 1253/3780 0.150 31.623 0.03162277660168379 StandardScaler() 0 -> 0.543177315454534
 1254/3780 0.150 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5636466967030878
 1255/3780 0.150 31.623 0.1 StandardScaler() 0 -> 0.5583558059398981
 1256/3780 0.150 31.623 0.1 MinMaxScaler()   0 -> 0.5463593434091151
 1257/3780 0.150 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6459051418869562
 1258/3780 0.150 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5440939421673205
 1259/3780 0.150 31.623 1.0 StandardScaler() 0 -> 0.800131085501614
 1260/3780 0.150 31.623 1.0 MinMaxScaler()   0 -> 0.5425008853654553
 1261/3780 0.200 0.032 scale StandardScaler() 0 -> 0.5622653197354835
 1262/3780 0.200 0.032 scale MinMaxScaler()   0 -> 0.5585672200439152
 1263/3780 0.200 0.032 auto StandardScaler() 0 -> 0.5622653197354835
 1264/3780 0.200 0.032 auto MinMaxScaler()   0 -> 0.6265238411171098
 1265/3780 0.200 0.032 0.01 StandardScaler() 0 -> 0.6222374486875878
 1266/3780 0.200 0.032 0.01 MinMaxScaler()   0 -> 0.7524450140352453
 1267/3780 0.200 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5825495867260205
 1268/3780 0.200 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7207228999661192
 1269/3780 0.200 0.032 0.1 StandardScaler() 0 -> 0.5595398898964513
 1270/3780 0.200 0.032 0.1 MinMaxScaler()   0 -> 0.6539549309955975
 1271/3780 0.200 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5696165947649555
 1272/3780 0.200 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6088839648920472
 1273/3780 0.200 0.032 1.0 StandardScaler() 0 -> 0.6231445710509537
 1274/3780 0.200 0.032 1.0 MinMaxScaler()   0 -> 0.5747947729303794
 1275/3780 0.200 0.040 scale StandardScaler() 0 -> 0.5574250218332876
 1276/3780 0.200 0.040 scale MinMaxScaler()   0 -> 0.5540648132846713
 1277/3780 0.200 0.040 auto StandardScaler() 0 -> 0.5574250218332875
 1278/3780 0.200 0.040 auto MinMaxScaler()   0 -> 0.6163797520808918
 1279/3780 0.200 0.040 0.01 StandardScaler() 0 -> 0.6123583996338121
 1280/3780 0.200 0.040 0.01 MinMaxScaler()   0 -> 0.7482117888407264
 1281/3780 0.200 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5761738060268653
 1282/3780 0.200 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.709383845705025
 1283/3780 0.200 0.040 0.1 StandardScaler() 0 -> 0.5552058212669403
 1284/3780 0.200 0.040 0.1 MinMaxScaler()   0 -> 0.6437765369053389
 1285/3780 0.200 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5634795462256167
 1286/3780 0.200 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5998677893975805
 1287/3780 0.200 0.040 1.0 StandardScaler() 0 -> 0.6101930365895906
 1288/3780 0.200 0.040 1.0 MinMaxScaler()   0 -> 0.568751953759331
 1289/3780 0.200 0.051 scale StandardScaler() 0 -> 0.5533867547381042
 1290/3780 0.200 0.051 scale MinMaxScaler()   0 -> 0.550580345849322
 1291/3780 0.200 0.051 auto StandardScaler() 0 -> 0.5533867547381041
 1292/3780 0.200 0.051 auto MinMaxScaler()   0 -> 0.6070993084013286
 1293/3780 0.200 0.051 0.01 StandardScaler() 0 -> 0.6037277804457252
 1294/3780 0.200 0.051 0.01 MinMaxScaler()   0 -> 0.7429387095453205
 1295/3780 0.200 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5704476201534417
 1296/3780 0.200 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.695933782637522
 1297/3780 0.200 0.051 0.1 StandardScaler() 0 -> 0.5517356971909604
 1298/3780 0.200 0.051 0.1 MinMaxScaler()   0 -> 0.6345503230927038
 1299/3780 0.200 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5588272971470083
 1300/3780 0.200 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5925679066194516
 1301/3780 0.200 0.051 1.0 StandardScaler() 0 -> 0.5984692971626112
 1302/3780 0.200 0.051 1.0 MinMaxScaler()   0 -> 0.5636906196468418
 1303/3780 0.200 0.065 scale StandardScaler() 0 -> 0.550482333881496
 1304/3780 0.200 0.065 scale MinMaxScaler()   0 -> 0.5481144491110554
 1305/3780 0.200 0.065 auto StandardScaler() 0 -> 0.550482333881496
 1306/3780 0.200 0.065 auto MinMaxScaler()   0 -> 0.5989970208303909
 1307/3780 0.200 0.065 0.01 StandardScaler() 0 -> 0.5961538415542064
 1308/3780 0.200 0.065 0.01 MinMaxScaler()   0 -> 0.7364063400342259
 1309/3780 0.200 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5649442282290101
 1310/3780 0.200 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6804635679186735
 1311/3780 0.200 0.065 0.1 StandardScaler() 0 -> 0.5488526585752312
 1312/3780 0.200 0.065 0.1 MinMaxScaler()   0 -> 0.6242594635646252
 1313/3780 0.200 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5546856823223291
 1314/3780 0.200 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5869198391515046
 1315/3780 0.200 0.065 1.0 StandardScaler() 0 -> 0.5871761141480566
 1316/3780 0.200 0.065 1.0 MinMaxScaler()   0 -> 0.5596185384171717
 1317/3780 0.200 0.082 scale StandardScaler() 0 -> 0.5475531369508627
 1318/3780 0.200 0.082 scale MinMaxScaler()   0 -> 0.5456042278855834
 1319/3780 0.200 0.082 auto StandardScaler() 0 -> 0.5475531369508625
 1320/3780 0.200 0.082 auto MinMaxScaler()   0 -> 0.5929776007589375
 1321/3780 0.200 0.082 0.01 StandardScaler() 0 -> 0.5905421932750252
 1322/3780 0.200 0.082 0.01 MinMaxScaler()   0 -> 0.7283729891139522
 1323/3780 0.200 0.082 0.03162277660168379 StandardScaler() 0 -> 0.560986310863309
 1324/3780 0.200 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.664219043456021
 1325/3780 0.200 0.082 0.1 StandardScaler() 0 -> 0.5463406402836858
 1326/3780 0.200 0.082 0.1 MinMaxScaler()   0 -> 0.6144217606487582
 1327/3780 0.200 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5507334881340934
 1328/3780 0.200 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5835935280225278
 1329/3780 0.200 0.082 1.0 StandardScaler() 0 -> 0.5779013196826002
 1330/3780 0.200 0.082 1.0 MinMaxScaler()   0 -> 0.556302578135672
 1331/3780 0.200 0.104 scale StandardScaler() 0 -> 0.5453390972061837
 1332/3780 0.200 0.104 scale MinMaxScaler()   0 -> 0.5433375523197139
 1333/3780 0.200 0.104 auto StandardScaler() 0 -> 0.5453390972061835
 1334/3780 0.200 0.104 auto MinMaxScaler()   0 -> 0.5881875870348625
 1335/3780 0.200 0.104 0.01 StandardScaler() 0 -> 0.5856000120935286
 1336/3780 0.200 0.104 0.01 MinMaxScaler()   0 -> 0.7185920826034061
 1337/3780 0.200 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5569756213234496
 1338/3780 0.200 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.651248332283955
 1339/3780 0.200 0.104 0.1 StandardScaler() 0 -> 0.5441491320972127
 1340/3780 0.200 0.104 0.1 MinMaxScaler()   0 -> 0.606089186776398
 1341/3780 0.200 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5476456256225984
 1342/3780 0.200 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.58041469002904
 1343/3780 0.200 0.104 1.0 StandardScaler() 0 -> 0.569006534752568
 1344/3780 0.200 0.104 1.0 MinMaxScaler()   0 -> 0.5533304199052647
 1345/3780 0.200 0.132 scale StandardScaler() 0 -> 0.5435510246480645
 1346/3780 0.200 0.132 scale MinMaxScaler()   0 -> 0.5416128105134582
 1347/3780 0.200 0.132 auto StandardScaler() 0 -> 0.5435510246480645
 1348/3780 0.200 0.132 auto MinMaxScaler()   0 -> 0.5852020992337931
 1349/3780 0.200 0.132 0.01 StandardScaler() 0 -> 0.5816167652119447
 1350/3780 0.200 0.132 0.01 MinMaxScaler()   0 -> 0.7068423666151554
 1351/3780 0.200 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5537109653889813
 1352/3780 0.200 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6412426441073295
 1353/3780 0.200 0.132 0.1 StandardScaler() 0 -> 0.5427755678582603
 1354/3780 0.200 0.132 0.1 MinMaxScaler()   0 -> 0.5988372301156915
 1355/3780 0.200 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5443748619213347
 1356/3780 0.200 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5767872700000943
 1357/3780 0.200 0.132 1.0 StandardScaler() 0 -> 0.5609684699918959
 1358/3780 0.200 0.132 1.0 MinMaxScaler()   0 -> 0.550421478763016
 1359/3780 0.200 0.168 scale StandardScaler() 0 -> 0.5417788486243283
 1360/3780 0.200 0.168 scale MinMaxScaler()   0 -> 0.5401506387217222
 1361/3780 0.200 0.168 auto StandardScaler() 0 -> 0.5417788486243283
 1362/3780 0.200 0.168 auto MinMaxScaler()   0 -> 0.5828236531745087
 1363/3780 0.200 0.168 0.01 StandardScaler() 0 -> 0.5783404331012081
 1364/3780 0.200 0.168 0.01 MinMaxScaler()   0 -> 0.6930006327247046
 1365/3780 0.200 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5510873800315897
 1366/3780 0.200 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6320638456235373
 1367/3780 0.200 0.168 0.1 StandardScaler() 0 -> 0.5417816855853343
 1368/3780 0.200 0.168 0.1 MinMaxScaler()   0 -> 0.5935012454213872
 1369/3780 0.200 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5414457191398367
 1370/3780 0.200 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5734908091052193
 1371/3780 0.200 0.168 1.0 StandardScaler() 0 -> 0.5543368950213502
 1372/3780 0.200 0.168 1.0 MinMaxScaler()   0 -> 0.5477376646166988
 1373/3780 0.200 0.213 scale StandardScaler() 0 -> 0.5405305377790537
 1374/3780 0.200 0.213 scale MinMaxScaler()   0 -> 0.5391223489905554
 1375/3780 0.200 0.213 auto StandardScaler() 0 -> 0.5405305377790535
 1376/3780 0.200 0.213 auto MinMaxScaler()   0 -> 0.5803173038372512
 1377/3780 0.200 0.213 0.01 StandardScaler() 0 -> 0.5749700583623997
 1378/3780 0.200 0.213 0.01 MinMaxScaler()   0 -> 0.6772558199851907
 1379/3780 0.200 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5482136563353334
 1380/3780 0.200 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6216828621781055
 1381/3780 0.200 0.213 0.1 StandardScaler() 0 -> 0.5412162693608015
 1382/3780 0.200 0.213 0.1 MinMaxScaler()   0 -> 0.5899186521891139
 1383/3780 0.200 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5393488481619156
 1384/3780 0.200 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.570071509904968
 1385/3780 0.200 0.213 1.0 StandardScaler() 0 -> 0.5497340121458657
 1386/3780 0.200 0.213 1.0 MinMaxScaler()   0 -> 0.5463415512683982
 1387/3780 0.200 0.270 scale StandardScaler() 0 -> 0.5394535194062077
 1388/3780 0.200 0.270 scale MinMaxScaler()   0 -> 0.5374982782234398
 1389/3780 0.200 0.270 auto StandardScaler() 0 -> 0.5394535194062077
 1390/3780 0.200 0.270 auto MinMaxScaler()   0 -> 0.5774738277623511
 1391/3780 0.200 0.270 0.01 StandardScaler() 0 -> 0.5714722371173534
 1392/3780 0.200 0.270 0.01 MinMaxScaler()   0 -> 0.6611736468009409
 1393/3780 0.200 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5457050499948215
 1394/3780 0.200 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6125066231806414
 1395/3780 0.200 0.270 0.1 StandardScaler() 0 -> 0.5403223906614442
 1396/3780 0.200 0.270 0.1 MinMaxScaler()   0 -> 0.5873937978107849
 1397/3780 0.200 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5377601988557428
 1398/3780 0.200 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5668741963158869
 1399/3780 0.200 0.270 1.0 StandardScaler() 0 -> 0.546348652876422
 1400/3780 0.200 0.270 1.0 MinMaxScaler()   0 -> 0.5439926961191933
 1401/3780 0.200 0.342 scale StandardScaler() 0 -> 0.5381446465558856
 1402/3780 0.200 0.342 scale MinMaxScaler()   0 -> 0.53622624134396
 1403/3780 0.200 0.342 auto StandardScaler() 0 -> 0.5381446465558853
 1404/3780 0.200 0.342 auto MinMaxScaler()   0 -> 0.5750341198040014
 1405/3780 0.200 0.342 0.01 StandardScaler() 0 -> 0.5678446656879733
 1406/3780 0.200 0.342 0.01 MinMaxScaler()   0 -> 0.649294831441745
 1407/3780 0.200 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5446784511216459
 1408/3780 0.200 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.6047067900211358
 1409/3780 0.200 0.342 0.1 StandardScaler() 0 -> 0.5400143411029253
 1410/3780 0.200 0.342 0.1 MinMaxScaler()   0 -> 0.5856366835132998
 1411/3780 0.200 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5368438929246363
 1412/3780 0.200 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5636175668639775
 1413/3780 0.200 0.342 1.0 StandardScaler() 0 -> 0.5438041721761064
 1414/3780 0.200 0.342 1.0 MinMaxScaler()   0 -> 0.5429844993331531
 1415/3780 0.200 0.434 scale StandardScaler() 0 -> 0.5369186566337065
 1416/3780 0.200 0.434 scale MinMaxScaler()   0 -> 0.5350443784958149
 1417/3780 0.200 0.434 auto StandardScaler() 0 -> 0.5369186566337065
 1418/3780 0.200 0.434 auto MinMaxScaler()   0 -> 0.5722557354743584
 1419/3780 0.200 0.434 0.01 StandardScaler() 0 -> 0.564435627884874
 1420/3780 0.200 0.434 0.01 MinMaxScaler()   0 -> 0.639328682611538
 1421/3780 0.200 0.434 0.03162277660168379 StandardScaler() 0 -> 0.543394594344719
 1422/3780 0.200 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5982325061362329
 1423/3780 0.200 0.434 0.1 StandardScaler() 0 -> 0.5397031039767968
 1424/3780 0.200 0.434 0.1 MinMaxScaler()   0 -> 0.5835925128744123
 1425/3780 0.200 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5364720770043726
 1426/3780 0.200 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5600461013094877
 1427/3780 0.200 0.434 1.0 StandardScaler() 0 -> 0.5430360359465104
 1428/3780 0.200 0.434 1.0 MinMaxScaler()   0 -> 0.5420091520431077
 1429/3780 0.200 0.551 scale StandardScaler() 0 -> 0.536367996905677
 1430/3780 0.200 0.551 scale MinMaxScaler()   0 -> 0.5350078043956604
 1431/3780 0.200 0.551 auto StandardScaler() 0 -> 0.5363679969056768
 1432/3780 0.200 0.551 auto MinMaxScaler()   0 -> 0.5690365048839338
 1433/3780 0.200 0.551 0.01 StandardScaler() 0 -> 0.5611348328526206
 1434/3780 0.200 0.551 0.01 MinMaxScaler()   0 -> 0.6300581932148538
 1435/3780 0.200 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5426078759075142
 1436/3780 0.200 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5935857184322911
 1437/3780 0.200 0.551 0.1 StandardScaler() 0 -> 0.5389174747609883
 1438/3780 0.200 0.551 0.1 MinMaxScaler()   0 -> 0.5823388779452799
 1439/3780 0.200 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5365344597008611
 1440/3780 0.200 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5570083999614335
 1441/3780 0.200 0.551 1.0 StandardScaler() 0 -> 0.5429198454665074
 1442/3780 0.200 0.551 1.0 MinMaxScaler()   0 -> 0.5413972855394444
 1443/3780 0.200 0.700 scale StandardScaler() 0 -> 0.535936164418925
 1444/3780 0.200 0.700 scale MinMaxScaler()   0 -> 0.5344728692149373
 1445/3780 0.200 0.700 auto StandardScaler() 0 -> 0.5359361644189246
 1446/3780 0.200 0.700 auto MinMaxScaler()   0 -> 0.5658150551959467
 1447/3780 0.200 0.700 0.01 StandardScaler() 0 -> 0.5582934730295144
 1448/3780 0.200 0.700 0.01 MinMaxScaler()   0 -> 0.6198990797351662
 1449/3780 0.200 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5420481808501371
 1450/3780 0.200 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5906667950226393
 1451/3780 0.200 0.700 0.1 StandardScaler() 0 -> 0.5380872767734625
 1452/3780 0.200 0.700 0.1 MinMaxScaler()   0 -> 0.5808791508196843
 1453/3780 0.200 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5366852224450113
 1454/3780 0.200 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5541596747719916
 1455/3780 0.200 0.700 1.0 StandardScaler() 0 -> 0.544302463119553
 1456/3780 0.200 0.700 1.0 MinMaxScaler()   0 -> 0.5413315006358052
 1457/3780 0.200 0.888 scale StandardScaler() 0 -> 0.5363111086064483
 1458/3780 0.200 0.888 scale MinMaxScaler()   0 -> 0.5348507088775344
 1459/3780 0.200 0.888 auto StandardScaler() 0 -> 0.5363111086064484
 1460/3780 0.200 0.888 auto MinMaxScaler()   0 -> 0.5624117733791295
 1461/3780 0.200 0.888 0.01 StandardScaler() 0 -> 0.5556111313736009
 1462/3780 0.200 0.888 0.01 MinMaxScaler()   0 -> 0.6112233881530905
 1463/3780 0.200 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5422125065193796
 1464/3780 0.200 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5888740438630251
 1465/3780 0.200 0.888 0.1 StandardScaler() 0 -> 0.5378964545363285
 1466/3780 0.200 0.888 0.1 MinMaxScaler()   0 -> 0.5788401613904166
 1467/3780 0.200 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5386161153017
 1468/3780 0.200 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5524086723746694
 1469/3780 0.200 0.888 1.0 StandardScaler() 0 -> 0.5480027652787846
 1470/3780 0.200 0.888 1.0 MinMaxScaler()   0 -> 0.5409045193479766
 1471/3780 0.200 1.126 scale StandardScaler() 0 -> 0.5372746719318428
 1472/3780 0.200 1.126 scale MinMaxScaler()   0 -> 0.5353193928662812
 1473/3780 0.200 1.126 auto StandardScaler() 0 -> 0.5372746719318425
 1474/3780 0.200 1.126 auto MinMaxScaler()   0 -> 0.5590556673943611
 1475/3780 0.200 1.126 0.01 StandardScaler() 0 -> 0.5532809624441258
 1476/3780 0.200 1.126 0.01 MinMaxScaler()   0 -> 0.6034319104827373
 1477/3780 0.200 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5418805097462908
 1478/3780 0.200 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5875940600825502
 1479/3780 0.200 1.126 0.1 StandardScaler() 0 -> 0.5375453881596867
 1480/3780 0.200 1.126 0.1 MinMaxScaler()   0 -> 0.5765641085196443
 1481/3780 0.200 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5408923783445193
 1482/3780 0.200 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5501861066283694
 1483/3780 0.200 1.126 1.0 StandardScaler() 0 -> 0.5527368961221036
 1484/3780 0.200 1.126 1.0 MinMaxScaler()   0 -> 0.5410104211519797
 1485/3780 0.200 1.429 scale StandardScaler() 0 -> 0.5383768951240945
 1486/3780 0.200 1.429 scale MinMaxScaler()   0 -> 0.5365943287770134
 1487/3780 0.200 1.429 auto StandardScaler() 0 -> 0.5383768951240943
 1488/3780 0.200 1.429 auto MinMaxScaler()   0 -> 0.556057410632713
 1489/3780 0.200 1.429 0.01 StandardScaler() 0 -> 0.5502649579696587
 1490/3780 0.200 1.429 0.01 MinMaxScaler()   0 -> 0.5972291574558528
 1491/3780 0.200 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5415163975716245
 1492/3780 0.200 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5866224955575373
 1493/3780 0.200 1.429 0.1 StandardScaler() 0 -> 0.5376504571330994
 1494/3780 0.200 1.429 0.1 MinMaxScaler()   0 -> 0.5741870359588382
 1495/3780 0.200 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5427616674707546
 1496/3780 0.200 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5482256985952841
 1497/3780 0.200 1.429 1.0 StandardScaler() 0 -> 0.5599078100088337
 1498/3780 0.200 1.429 1.0 MinMaxScaler()   0 -> 0.5407158768725554
 1499/3780 0.200 1.814 scale StandardScaler() 0 -> 0.5401089981115237
 1500/3780 0.200 1.814 scale MinMaxScaler()   0 -> 0.5381033469130588
 1501/3780 0.200 1.814 auto StandardScaler() 0 -> 0.5401089981115241
 1502/3780 0.200 1.814 auto MinMaxScaler()   0 -> 0.5534553527751194
 1503/3780 0.200 1.814 0.01 StandardScaler() 0 -> 0.5479312436819023
 1504/3780 0.200 1.814 0.01 MinMaxScaler()   0 -> 0.5934077309862283
 1505/3780 0.200 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5414293240301643
 1506/3780 0.200 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5862856239589772
 1507/3780 0.200 1.814 0.1 StandardScaler() 0 -> 0.5377182793169138
 1508/3780 0.200 1.814 0.1 MinMaxScaler()   0 -> 0.5716565887047619
 1509/3780 0.200 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5449955741832148
 1510/3780 0.200 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5459211363002595
 1511/3780 0.200 1.814 1.0 StandardScaler() 0 -> 0.5681583602713303
 1512/3780 0.200 1.814 1.0 MinMaxScaler()   0 -> 0.5405703100054553
 1513/3780 0.200 2.302 scale StandardScaler() 0 -> 0.5421091336483993
 1514/3780 0.200 2.302 scale MinMaxScaler()   0 -> 0.5395643661697217
 1515/3780 0.200 2.302 auto StandardScaler() 0 -> 0.5421091336483997
 1516/3780 0.200 2.302 auto MinMaxScaler()   0 -> 0.5520355879393469
 1517/3780 0.200 2.302 0.01 StandardScaler() 0 -> 0.5461165128201307
 1518/3780 0.200 2.302 0.01 MinMaxScaler()   0 -> 0.5909043627703778
 1519/3780 0.200 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5415801555659852
 1520/3780 0.200 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5854240258824426
 1521/3780 0.200 2.302 0.1 StandardScaler() 0 -> 0.5382216562139768
 1522/3780 0.200 2.302 0.1 MinMaxScaler()   0 -> 0.5687251033636863
 1523/3780 0.200 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5486176480652619
 1524/3780 0.200 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.544316692999263
 1525/3780 0.200 2.302 1.0 StandardScaler() 0 -> 0.5759239949450538
 1526/3780 0.200 2.302 1.0 MinMaxScaler()   0 -> 0.5407694980156865
 1527/3780 0.200 2.921 scale StandardScaler() 0 -> 0.5448973538850961
 1528/3780 0.200 2.921 scale MinMaxScaler()   0 -> 0.5413642546200124
 1529/3780 0.200 2.921 auto StandardScaler() 0 -> 0.5448973538850952
 1530/3780 0.200 2.921 auto MinMaxScaler()   0 -> 0.5499653275995167
 1531/3780 0.200 2.921 0.01 StandardScaler() 0 -> 0.5449864857386899
 1532/3780 0.200 2.921 0.01 MinMaxScaler()   0 -> 0.5893455764953097
 1533/3780 0.200 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5414650278704162
 1534/3780 0.200 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5844492919531382
 1535/3780 0.200 2.921 0.1 StandardScaler() 0 -> 0.5381160630927839
 1536/3780 0.200 2.921 0.1 MinMaxScaler()   0 -> 0.5650797422289069
 1537/3780 0.200 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5534423689834383
 1538/3780 0.200 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5437255112270822
 1539/3780 0.200 2.921 1.0 StandardScaler() 0 -> 0.5860033536220667
 1540/3780 0.200 2.921 1.0 MinMaxScaler()   0 -> 0.5407768362288313
 1541/3780 0.200 3.707 scale StandardScaler() 0 -> 0.5471276215369115
 1542/3780 0.200 3.707 scale MinMaxScaler()   0 -> 0.5434405047884683
 1543/3780 0.200 3.707 auto StandardScaler() 0 -> 0.5471276215369105
 1544/3780 0.200 3.707 auto MinMaxScaler()   0 -> 0.5480609117253875
 1545/3780 0.200 3.707 0.01 StandardScaler() 0 -> 0.5450334125936281
 1546/3780 0.200 3.707 0.01 MinMaxScaler()   0 -> 0.5881806935128239
 1547/3780 0.200 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5413785478106267
 1548/3780 0.200 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5836196869936807
 1549/3780 0.200 3.707 0.1 StandardScaler() 0 -> 0.5388893438885743
 1550/3780 0.200 3.707 0.1 MinMaxScaler()   0 -> 0.5614872233763709
 1551/3780 0.200 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5582344905517375
 1552/3780 0.200 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5435466679192685
 1553/3780 0.200 3.707 1.0 StandardScaler() 0 -> 0.5953165038783482
 1554/3780 0.200 3.707 1.0 MinMaxScaler()   0 -> 0.5406391105752218
 1555/3780 0.200 4.703 scale StandardScaler() 0 -> 0.5496253149422529
 1556/3780 0.200 4.703 scale MinMaxScaler()   0 -> 0.5472485411913853
 1557/3780 0.200 4.703 auto StandardScaler() 0 -> 0.5496253149422513
 1558/3780 0.200 4.703 auto MinMaxScaler()   0 -> 0.5460978479862281
 1559/3780 0.200 4.703 0.01 StandardScaler() 0 -> 0.5448834565844739
 1560/3780 0.200 4.703 0.01 MinMaxScaler()   0 -> 0.5876751700624278
 1561/3780 0.200 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5410823653509366
 1562/3780 0.200 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5826438447472639
 1563/3780 0.200 4.703 0.1 StandardScaler() 0 -> 0.5402096637402404
 1564/3780 0.200 4.703 0.1 MinMaxScaler()   0 -> 0.558236838312606
 1565/3780 0.200 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5627302494203507
 1566/3780 0.200 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5433470517744988
 1567/3780 0.200 4.703 1.0 StandardScaler() 0 -> 0.6065607438267152
 1568/3780 0.200 4.703 1.0 MinMaxScaler()   0 -> 0.5403742487587017
 1569/3780 0.200 5.968 scale StandardScaler() 0 -> 0.5526743548564895
 1570/3780 0.200 5.968 scale MinMaxScaler()   0 -> 0.550282183464672
 1571/3780 0.200 5.968 auto StandardScaler() 0 -> 0.5526743548564917
 1572/3780 0.200 5.968 auto MinMaxScaler()   0 -> 0.5448549288759926
 1573/3780 0.200 5.968 0.01 StandardScaler() 0 -> 0.5445765503429998
 1574/3780 0.200 5.968 0.01 MinMaxScaler()   0 -> 0.5875676990407972
 1575/3780 0.200 5.968 0.03162277660168379 StandardScaler() 0 -> 0.540606608716476
 1576/3780 0.200 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5814386534750943
 1577/3780 0.200 5.968 0.1 StandardScaler() 0 -> 0.5411770840484271
 1578/3780 0.200 5.968 0.1 MinMaxScaler()   0 -> 0.5555487612135611
 1579/3780 0.200 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5687048481504176
 1580/3780 0.200 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.543415253643586
 1581/3780 0.200 5.968 1.0 StandardScaler() 0 -> 0.6199592277714714
 1582/3780 0.200 5.968 1.0 MinMaxScaler()   0 -> 0.5402325914008701
 1583/3780 0.200 7.574 scale StandardScaler() 0 -> 0.5566998266729279
 1584/3780 0.200 7.574 scale MinMaxScaler()   0 -> 0.5532377256686937
 1585/3780 0.200 7.574 auto StandardScaler() 0 -> 0.5566998266729297
 1586/3780 0.200 7.574 auto MinMaxScaler()   0 -> 0.5444234521099433
 1587/3780 0.200 7.574 0.01 StandardScaler() 0 -> 0.5444959343408423
 1588/3780 0.200 7.574 0.01 MinMaxScaler()   0 -> 0.5870790861012443
 1589/3780 0.200 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5398093356728083
 1590/3780 0.200 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.579970149120692
 1591/3780 0.200 7.574 0.1 StandardScaler() 0 -> 0.5432861122654397
 1592/3780 0.200 7.574 0.1 MinMaxScaler()   0 -> 0.5532666888277004
 1593/3780 0.200 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5765160596034099
 1594/3780 0.200 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5434802849036877
 1595/3780 0.200 7.574 1.0 StandardScaler() 0 -> 0.6356040155967713
 1596/3780 0.200 7.574 1.0 MinMaxScaler()   0 -> 0.54024100250229
 1597/3780 0.200 9.611 scale StandardScaler() 0 -> 0.561482468986499
 1598/3780 0.200 9.611 scale MinMaxScaler()   0 -> 0.5571520866383007
 1599/3780 0.200 9.611 auto StandardScaler() 0 -> 0.5614824689864921
 1600/3780 0.200 9.611 auto MinMaxScaler()   0 -> 0.5443537178631385
 1601/3780 0.200 9.611 0.01 StandardScaler() 0 -> 0.5443940961218234
 1602/3780 0.200 9.611 0.01 MinMaxScaler()   0 -> 0.5868637236068621
 1603/3780 0.200 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5398267012885632
 1604/3780 0.200 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5781565488357789
 1605/3780 0.200 9.611 0.1 StandardScaler() 0 -> 0.5446050881142516
 1606/3780 0.200 9.611 0.1 MinMaxScaler()   0 -> 0.5518096573880605
 1607/3780 0.200 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5834562029021234
 1608/3780 0.200 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5434472826585979
 1609/3780 0.200 9.611 1.0 StandardScaler() 0 -> 0.6525103158274604
 1610/3780 0.200 9.611 1.0 MinMaxScaler()   0 -> 0.5399999554047792
 1611/3780 0.200 12.196 scale StandardScaler() 0 -> 0.5670412331373048
 1612/3780 0.200 12.196 scale MinMaxScaler()   0 -> 0.5611860841004997
 1613/3780 0.200 12.196 auto StandardScaler() 0 -> 0.5670412331373047
 1614/3780 0.200 12.196 auto MinMaxScaler()   0 -> 0.5441762766074435
 1615/3780 0.200 12.196 0.01 StandardScaler() 0 -> 0.5443358600215563
 1616/3780 0.200 12.196 0.01 MinMaxScaler()   0 -> 0.5865235832468386
 1617/3780 0.200 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5397092528110692
 1618/3780 0.200 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5761186162178272
 1619/3780 0.200 12.196 0.1 StandardScaler() 0 -> 0.5462003343973568
 1620/3780 0.200 12.196 0.1 MinMaxScaler()   0 -> 0.5498674980760621
 1621/3780 0.200 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5906626164365546
 1622/3780 0.200 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5433119703877044
 1623/3780 0.200 12.196 1.0 StandardScaler() 0 -> 0.6713242932384359
 1624/3780 0.200 12.196 1.0 MinMaxScaler()   0 -> 0.5397183913327064
 1625/3780 0.200 15.476 scale StandardScaler() 0 -> 0.5730206976304498
 1626/3780 0.200 15.476 scale MinMaxScaler()   0 -> 0.565037114008912
 1627/3780 0.200 15.476 auto StandardScaler() 0 -> 0.5730206976304534
 1628/3780 0.200 15.476 auto MinMaxScaler()   0 -> 0.5443102967612344
 1629/3780 0.200 15.476 0.01 StandardScaler() 0 -> 0.5446701408800761
 1630/3780 0.200 15.476 0.01 MinMaxScaler()   0 -> 0.5862419097890729
 1631/3780 0.200 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5394751365906457
 1632/3780 0.200 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5733994419002474
 1633/3780 0.200 15.476 0.1 StandardScaler() 0 -> 0.5468001709913344
 1634/3780 0.200 15.476 0.1 MinMaxScaler()   0 -> 0.5481017931201427
 1635/3780 0.200 15.476 0.31622776601683794 StandardScaler() 0 -> 0.599176136355052
 1636/3780 0.200 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5432052656738001
 1637/3780 0.200 15.476 1.0 StandardScaler() 0 -> 0.6931946650206419
 1638/3780 0.200 15.476 1.0 MinMaxScaler()   0 -> 0.5397775469758442
 1639/3780 0.200 19.638 scale StandardScaler() 0 -> 0.5806778382712822
 1640/3780 0.200 19.638 scale MinMaxScaler()   0 -> 0.568444048334317
 1641/3780 0.200 19.638 auto StandardScaler() 0 -> 0.5806778382712813
 1642/3780 0.200 19.638 auto MinMaxScaler()   0 -> 0.5443916340179186
 1643/3780 0.200 19.638 0.01 StandardScaler() 0 -> 0.5445547805661692
 1644/3780 0.200 19.638 0.01 MinMaxScaler()   0 -> 0.5859027663662455
 1645/3780 0.200 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5399371335975555
 1646/3780 0.200 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5708790738991212
 1647/3780 0.200 19.638 0.1 StandardScaler() 0 -> 0.5486240103602955
 1648/3780 0.200 19.638 0.1 MinMaxScaler()   0 -> 0.546303954167929
 1649/3780 0.200 19.638 0.31622776601683794 StandardScaler() 0 -> 0.6100553671104477
 1650/3780 0.200 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5431329661090661
 1651/3780 0.200 19.638 1.0 StandardScaler() 0 -> 0.7175252352166593
 1652/3780 0.200 19.638 1.0 MinMaxScaler()   0 -> 0.5397155000445345
 1653/3780 0.200 24.920 scale StandardScaler() 0 -> 0.5886529856902555
 1654/3780 0.200 24.920 scale MinMaxScaler()   0 -> 0.572918027852096
 1655/3780 0.200 24.920 auto StandardScaler() 0 -> 0.5886529856902488
 1656/3780 0.200 24.920 auto MinMaxScaler()   0 -> 0.5443078192908449
 1657/3780 0.200 24.920 0.01 StandardScaler() 0 -> 0.5447623701779672
 1658/3780 0.200 24.920 0.01 MinMaxScaler()   0 -> 0.585219009393061
 1659/3780 0.200 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5402619940997816
 1660/3780 0.200 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5674372316021405
 1661/3780 0.200 24.920 0.1 StandardScaler() 0 -> 0.551990243369201
 1662/3780 0.200 24.920 0.1 MinMaxScaler()   0 -> 0.5456974000192568
 1663/3780 0.200 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6232589452619252
 1664/3780 0.200 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.543156755696747
 1665/3780 0.200 24.920 1.0 StandardScaler() 0 -> 0.7453315743287258
 1666/3780 0.200 24.920 1.0 MinMaxScaler()   0 -> 0.5398326078791408
 1667/3780 0.200 31.623 scale StandardScaler() 0 -> 0.5970282070780498
 1668/3780 0.200 31.623 scale MinMaxScaler()   0 -> 0.5778350962172735
 1669/3780 0.200 31.623 auto StandardScaler() 0 -> 0.5970282070780683
 1670/3780 0.200 31.623 auto MinMaxScaler()   0 -> 0.5440918290453978
 1671/3780 0.200 31.623 0.01 StandardScaler() 0 -> 0.5445626558874181
 1672/3780 0.200 31.623 0.01 MinMaxScaler()   0 -> 0.5846219425622063
 1673/3780 0.200 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5407005569319915
 1674/3780 0.200 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5636542615086481
 1675/3780 0.200 31.623 0.1 StandardScaler() 0 -> 0.5555782174054918
 1676/3780 0.200 31.623 0.1 MinMaxScaler()   0 -> 0.5451229774360122
 1677/3780 0.200 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6388532203077223
 1678/3780 0.200 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5431298463896302
 1679/3780 0.200 31.623 1.0 StandardScaler() 0 -> 0.7745271376339066
 1680/3780 0.200 31.623 1.0 MinMaxScaler()   0 -> 0.5401577534190921
 1681/3780 0.250 0.032 scale StandardScaler() 0 -> 0.5602781187578453
 1682/3780 0.250 0.032 scale MinMaxScaler()   0 -> 0.556742682121054
 1683/3780 0.250 0.032 auto StandardScaler() 0 -> 0.5602781187578453
 1684/3780 0.250 0.032 auto MinMaxScaler()   0 -> 0.6173120184238532
 1685/3780 0.250 0.032 0.01 StandardScaler() 0 -> 0.6136953086375486
 1686/3780 0.250 0.032 0.01 MinMaxScaler()   0 -> 0.7648763163408893
 1687/3780 0.250 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5794322947650318
 1688/3780 0.250 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7302558598123974
 1689/3780 0.250 0.032 0.1 StandardScaler() 0 -> 0.5590494886851292
 1690/3780 0.250 0.032 0.1 MinMaxScaler()   0 -> 0.6517080203322411
 1691/3780 0.250 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5671004972888958
 1692/3780 0.250 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6004283283500623
 1693/3780 0.250 0.032 1.0 StandardScaler() 0 -> 0.6227788122282772
 1694/3780 0.250 0.032 1.0 MinMaxScaler()   0 -> 0.5725706197787609
 1695/3780 0.250 0.040 scale StandardScaler() 0 -> 0.5560884327623017
 1696/3780 0.250 0.040 scale MinMaxScaler()   0 -> 0.5529798120834213
 1697/3780 0.250 0.040 auto StandardScaler() 0 -> 0.5560884327623016
 1698/3780 0.250 0.040 auto MinMaxScaler()   0 -> 0.6077908704170493
 1699/3780 0.250 0.040 0.01 StandardScaler() 0 -> 0.6048466881482796
 1700/3780 0.250 0.040 0.01 MinMaxScaler()   0 -> 0.7602776135791901
 1701/3780 0.250 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5740776198198253
 1702/3780 0.250 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7177301152267838
 1703/3780 0.250 0.040 0.1 StandardScaler() 0 -> 0.5553530029864264
 1704/3780 0.250 0.040 0.1 MinMaxScaler()   0 -> 0.6353008803308554
 1705/3780 0.250 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5618224145008228
 1706/3780 0.250 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5940346699387054
 1707/3780 0.250 0.040 1.0 StandardScaler() 0 -> 0.6084277305429059
 1708/3780 0.250 0.040 1.0 MinMaxScaler()   0 -> 0.5676128581282315
 1709/3780 0.250 0.051 scale StandardScaler() 0 -> 0.5522352601661321
 1710/3780 0.250 0.051 scale MinMaxScaler()   0 -> 0.5500042512188208
 1711/3780 0.250 0.051 auto StandardScaler() 0 -> 0.5522352601661321
 1712/3780 0.250 0.051 auto MinMaxScaler()   0 -> 0.5999271416987685
 1713/3780 0.250 0.051 0.01 StandardScaler() 0 -> 0.597167016688885
 1714/3780 0.250 0.051 0.01 MinMaxScaler()   0 -> 0.7545407575034981
 1715/3780 0.250 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5695631432130338
 1716/3780 0.250 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.702850289916825
 1717/3780 0.250 0.051 0.1 StandardScaler() 0 -> 0.551333985168342
 1718/3780 0.250 0.051 0.1 MinMaxScaler()   0 -> 0.6244700031612168
 1719/3780 0.250 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5570449563188419
 1720/3780 0.250 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5887841022067657
 1721/3780 0.250 0.051 1.0 StandardScaler() 0 -> 0.5959414889113483
 1722/3780 0.250 0.051 1.0 MinMaxScaler()   0 -> 0.5637203402282139
 1723/3780 0.250 0.065 scale StandardScaler() 0 -> 0.5492433383924139
 1724/3780 0.250 0.065 scale MinMaxScaler()   0 -> 0.5473487503256512
 1725/3780 0.250 0.065 auto StandardScaler() 0 -> 0.5492433383924139
 1726/3780 0.250 0.065 auto MinMaxScaler()   0 -> 0.5940410325424259
 1727/3780 0.250 0.065 0.01 StandardScaler() 0 -> 0.5914543506506743
 1728/3780 0.250 0.065 0.01 MinMaxScaler()   0 -> 0.7474198733440461
 1729/3780 0.250 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5649656553072099
 1730/3780 0.250 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6855313328282455
 1731/3780 0.250 0.065 0.1 StandardScaler() 0 -> 0.5486471913528053
 1732/3780 0.250 0.065 0.1 MinMaxScaler()   0 -> 0.6155512840872377
 1733/3780 0.250 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5525965216623487
 1734/3780 0.250 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5850478387361081
 1735/3780 0.250 0.065 1.0 StandardScaler() 0 -> 0.584889541662056
 1736/3780 0.250 0.065 1.0 MinMaxScaler()   0 -> 0.560729360982414
 1737/3780 0.250 0.082 scale StandardScaler() 0 -> 0.5467886476009648
 1738/3780 0.250 0.082 scale MinMaxScaler()   0 -> 0.5453431220845443
 1739/3780 0.250 0.082 auto StandardScaler() 0 -> 0.5467886476009649
 1740/3780 0.250 0.082 auto MinMaxScaler()   0 -> 0.5893025177291599
 1741/3780 0.250 0.082 0.01 StandardScaler() 0 -> 0.5871431803532291
 1742/3780 0.250 0.082 0.01 MinMaxScaler()   0 -> 0.7386427610136419
 1743/3780 0.250 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5616472642235065
 1744/3780 0.250 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6660786179208156
 1745/3780 0.250 0.082 0.1 StandardScaler() 0 -> 0.5454277493763446
 1746/3780 0.250 0.082 0.1 MinMaxScaler()   0 -> 0.60620304698115
 1747/3780 0.250 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5496021537914447
 1748/3780 0.250 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5813055076302395
 1749/3780 0.250 0.082 1.0 StandardScaler() 0 -> 0.574693793060649
 1750/3780 0.250 0.082 1.0 MinMaxScaler()   0 -> 0.5572780758787282
 1751/3780 0.250 0.104 scale StandardScaler() 0 -> 0.5446035885664543
 1752/3780 0.250 0.104 scale MinMaxScaler()   0 -> 0.5430261647308553
 1753/3780 0.250 0.104 auto StandardScaler() 0 -> 0.5446035885664542
 1754/3780 0.250 0.104 auto MinMaxScaler()   0 -> 0.5865419426132795
 1755/3780 0.250 0.104 0.01 StandardScaler() 0 -> 0.5833347306191198
 1756/3780 0.250 0.104 0.01 MinMaxScaler()   0 -> 0.7279102292318451
 1757/3780 0.250 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5580928971157958
 1758/3780 0.250 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6464348923848141
 1759/3780 0.250 0.104 0.1 StandardScaler() 0 -> 0.5434900649890785
 1760/3780 0.250 0.104 0.1 MinMaxScaler()   0 -> 0.5991844339471594
 1761/3780 0.250 0.104 0.31622776601683794 StandardScaler() 0 -> 0.546449789375158
 1762/3780 0.250 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5789795604252311
 1763/3780 0.250 0.104 1.0 StandardScaler() 0 -> 0.5666854736746375
 1764/3780 0.250 0.104 1.0 MinMaxScaler()   0 -> 0.553882609717188
 1765/3780 0.250 0.132 scale StandardScaler() 0 -> 0.542584715806374
 1766/3780 0.250 0.132 scale MinMaxScaler()   0 -> 0.5410870524921617
 1767/3780 0.250 0.132 auto StandardScaler() 0 -> 0.5425847158063742
 1768/3780 0.250 0.132 auto MinMaxScaler()   0 -> 0.583379116529224
 1769/3780 0.250 0.132 0.01 StandardScaler() 0 -> 0.5804040315422703
 1770/3780 0.250 0.132 0.01 MinMaxScaler()   0 -> 0.7149618861354295
 1771/3780 0.250 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5551182244085108
 1772/3780 0.250 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6323749970501145
 1773/3780 0.250 0.132 0.1 StandardScaler() 0 -> 0.5425339565195525
 1774/3780 0.250 0.132 0.1 MinMaxScaler()   0 -> 0.5940510890471737
 1775/3780 0.250 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5437062346112337
 1776/3780 0.250 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5759786399708303
 1777/3780 0.250 0.132 1.0 StandardScaler() 0 -> 0.5595620942874623
 1778/3780 0.250 0.132 1.0 MinMaxScaler()   0 -> 0.5510714482683857
 1779/3780 0.250 0.168 scale StandardScaler() 0 -> 0.5410745294761907
 1780/3780 0.250 0.168 scale MinMaxScaler()   0 -> 0.5396590598484196
 1781/3780 0.250 0.168 auto StandardScaler() 0 -> 0.5410745294761906
 1782/3780 0.250 0.168 auto MinMaxScaler()   0 -> 0.5813531338149756
 1783/3780 0.250 0.168 0.01 StandardScaler() 0 -> 0.5773125619845206
 1784/3780 0.250 0.168 0.01 MinMaxScaler()   0 -> 0.6995949732544015
 1785/3780 0.250 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5514451733516973
 1786/3780 0.250 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6219637680908302
 1787/3780 0.250 0.168 0.1 StandardScaler() 0 -> 0.5408493104187437
 1788/3780 0.250 0.168 0.1 MinMaxScaler()   0 -> 0.5902857710078426
 1789/3780 0.250 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5409759203976234
 1790/3780 0.250 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5730694399683162
 1791/3780 0.250 0.168 1.0 StandardScaler() 0 -> 0.5532171148834064
 1792/3780 0.250 0.168 1.0 MinMaxScaler()   0 -> 0.548807143524824
 1793/3780 0.250 0.213 scale StandardScaler() 0 -> 0.5400017407542443
 1794/3780 0.250 0.213 scale MinMaxScaler()   0 -> 0.5384023413674829
 1795/3780 0.250 0.213 auto StandardScaler() 0 -> 0.5400017407542442
 1796/3780 0.250 0.213 auto MinMaxScaler()   0 -> 0.5791547819727166
 1797/3780 0.250 0.213 0.01 StandardScaler() 0 -> 0.5746643520001529
 1798/3780 0.250 0.213 0.01 MinMaxScaler()   0 -> 0.6817715120358039
 1799/3780 0.250 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5489134950712763
 1800/3780 0.250 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6131678928446441
 1801/3780 0.250 0.213 0.1 StandardScaler() 0 -> 0.5403914092498081
 1802/3780 0.250 0.213 0.1 MinMaxScaler()   0 -> 0.5881590440248515
 1803/3780 0.250 0.213 0.31622776601683794 StandardScaler() 0 -> 0.539178316691558
 1804/3780 0.250 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5701063847683393
 1805/3780 0.250 0.213 1.0 StandardScaler() 0 -> 0.5490515325124878
 1806/3780 0.250 0.213 1.0 MinMaxScaler()   0 -> 0.5468390645045474
 1807/3780 0.250 0.270 scale StandardScaler() 0 -> 0.53873699819247
 1808/3780 0.250 0.270 scale MinMaxScaler()   0 -> 0.5369857448236316
 1809/3780 0.250 0.270 auto StandardScaler() 0 -> 0.5387369981924698
 1810/3780 0.250 0.270 auto MinMaxScaler()   0 -> 0.5769451753831668
 1811/3780 0.250 0.270 0.01 StandardScaler() 0 -> 0.5716779202780168
 1812/3780 0.250 0.270 0.01 MinMaxScaler()   0 -> 0.6622352252746646
 1813/3780 0.250 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5471307747536063
 1814/3780 0.250 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6045728472999253
 1815/3780 0.250 0.270 0.1 StandardScaler() 0 -> 0.5396564337944102
 1816/3780 0.250 0.270 0.1 MinMaxScaler()   0 -> 0.5856663064323541
 1817/3780 0.250 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5374037837240309
 1818/3780 0.250 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5669018548573393
 1819/3780 0.250 0.270 1.0 StandardScaler() 0 -> 0.5452421652606139
 1820/3780 0.250 0.270 1.0 MinMaxScaler()   0 -> 0.5447924682257962
 1821/3780 0.250 0.342 scale StandardScaler() 0 -> 0.5372729728743665
 1822/3780 0.250 0.342 scale MinMaxScaler()   0 -> 0.5357000694114421
 1823/3780 0.250 0.342 auto StandardScaler() 0 -> 0.5372729728743663
 1824/3780 0.250 0.342 auto MinMaxScaler()   0 -> 0.5745126216643562
 1825/3780 0.250 0.342 0.01 StandardScaler() 0 -> 0.5685730130863619
 1826/3780 0.250 0.342 0.01 MinMaxScaler()   0 -> 0.6430928958774774
 1827/3780 0.250 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5450156148989503
 1828/3780 0.250 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5983565112985683
 1829/3780 0.250 0.342 0.1 StandardScaler() 0 -> 0.5388020820534041
 1830/3780 0.250 0.342 0.1 MinMaxScaler()   0 -> 0.5840120914195382
 1831/3780 0.250 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5358361759105174
 1832/3780 0.250 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5641835216421419
 1833/3780 0.250 0.342 1.0 StandardScaler() 0 -> 0.5430101692512125
 1834/3780 0.250 0.342 1.0 MinMaxScaler()   0 -> 0.5428538608162009
 1835/3780 0.250 0.434 scale StandardScaler() 0 -> 0.5359983162923769
 1836/3780 0.250 0.434 scale MinMaxScaler()   0 -> 0.535351189436967
 1837/3780 0.250 0.434 auto StandardScaler() 0 -> 0.5359983162923769
 1838/3780 0.250 0.434 auto MinMaxScaler()   0 -> 0.571774815023533
 1839/3780 0.250 0.434 0.01 StandardScaler() 0 -> 0.565169047495175
 1840/3780 0.250 0.434 0.01 MinMaxScaler()   0 -> 0.6302067093100964
 1841/3780 0.250 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5433462812425754
 1842/3780 0.250 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5937115137224444
 1843/3780 0.250 0.434 0.1 StandardScaler() 0 -> 0.5377147173092423
 1844/3780 0.250 0.434 0.1 MinMaxScaler()   0 -> 0.582651552955988
 1845/3780 0.250 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5354716356159693
 1846/3780 0.250 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5613504473172715
 1847/3780 0.250 0.434 1.0 StandardScaler() 0 -> 0.5420679858475719
 1848/3780 0.250 0.434 1.0 MinMaxScaler()   0 -> 0.5411848539930096
 1849/3780 0.250 0.551 scale StandardScaler() 0 -> 0.5359041013430196
 1850/3780 0.250 0.551 scale MinMaxScaler()   0 -> 0.5344061853263309
 1851/3780 0.250 0.551 auto StandardScaler() 0 -> 0.5359041013430196
 1852/3780 0.250 0.551 auto MinMaxScaler()   0 -> 0.5690368658433754
 1853/3780 0.250 0.551 0.01 StandardScaler() 0 -> 0.5622466902023618
 1854/3780 0.250 0.551 0.01 MinMaxScaler()   0 -> 0.6203359259469122
 1855/3780 0.250 0.551 0.03162277660168379 StandardScaler() 0 -> 0.541633479036522
 1856/3780 0.250 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.590890798678668
 1857/3780 0.250 0.551 0.1 StandardScaler() 0 -> 0.5365453108267859
 1858/3780 0.250 0.551 0.1 MinMaxScaler()   0 -> 0.5812197633204188
 1859/3780 0.250 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5351986026951207
 1860/3780 0.250 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.558528452598129
 1861/3780 0.250 0.551 1.0 StandardScaler() 0 -> 0.5412838279091824
 1862/3780 0.250 0.551 1.0 MinMaxScaler()   0 -> 0.5404970093034266
 1863/3780 0.250 0.700 scale StandardScaler() 0 -> 0.5362551321605716
 1864/3780 0.250 0.700 scale MinMaxScaler()   0 -> 0.5337356119838635
 1865/3780 0.250 0.700 auto StandardScaler() 0 -> 0.5362551321605712
 1866/3780 0.250 0.700 auto MinMaxScaler()   0 -> 0.5661282939127622
 1867/3780 0.250 0.700 0.01 StandardScaler() 0 -> 0.558943393676052
 1868/3780 0.250 0.700 0.01 MinMaxScaler()   0 -> 0.6116048944134079
 1869/3780 0.250 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5409056673977719
 1870/3780 0.250 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5889663295594353
 1871/3780 0.250 0.700 0.1 StandardScaler() 0 -> 0.5362531379652367
 1872/3780 0.250 0.700 0.1 MinMaxScaler()   0 -> 0.5797626737185296
 1873/3780 0.250 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5348185529366699
 1874/3780 0.250 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5560562594847418
 1875/3780 0.250 0.700 1.0 StandardScaler() 0 -> 0.5422709291469673
 1876/3780 0.250 0.700 1.0 MinMaxScaler()   0 -> 0.539715980369089
 1877/3780 0.250 0.888 scale StandardScaler() 0 -> 0.5363414971453196
 1878/3780 0.250 0.888 scale MinMaxScaler()   0 -> 0.5340557172181744
 1879/3780 0.250 0.888 auto StandardScaler() 0 -> 0.5363414971453196
 1880/3780 0.250 0.888 auto MinMaxScaler()   0 -> 0.5635061569106616
 1881/3780 0.250 0.888 0.01 StandardScaler() 0 -> 0.556941124984049
 1882/3780 0.250 0.888 0.01 MinMaxScaler()   0 -> 0.6034797611439702
 1883/3780 0.250 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5404240570698904
 1884/3780 0.250 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5876627142174325
 1885/3780 0.250 0.888 0.1 StandardScaler() 0 -> 0.536475406039404
 1886/3780 0.250 0.888 0.1 MinMaxScaler()   0 -> 0.5780276728815282
 1887/3780 0.250 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5368983706904417
 1888/3780 0.250 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5538144476573148
 1889/3780 0.250 0.888 1.0 StandardScaler() 0 -> 0.545201774187333
 1890/3780 0.250 0.888 1.0 MinMaxScaler()   0 -> 0.5393038547887472
 1891/3780 0.250 1.126 scale StandardScaler() 0 -> 0.5371071845126548
 1892/3780 0.250 1.126 scale MinMaxScaler()   0 -> 0.5347277107930766
 1893/3780 0.250 1.126 auto StandardScaler() 0 -> 0.5371071845126545
 1894/3780 0.250 1.126 auto MinMaxScaler()   0 -> 0.560563931673205
 1895/3780 0.250 1.126 0.01 StandardScaler() 0 -> 0.5538463532440477
 1896/3780 0.250 1.126 0.01 MinMaxScaler()   0 -> 0.5976844426424712
 1897/3780 0.250 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5402098462512436
 1898/3780 0.250 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5865042999073299
 1899/3780 0.250 1.126 0.1 StandardScaler() 0 -> 0.5369845533665938
 1900/3780 0.250 1.126 0.1 MinMaxScaler()   0 -> 0.5761138252782102
 1901/3780 0.250 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5392999382505924
 1902/3780 0.250 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5511527053680546
 1903/3780 0.250 1.126 1.0 StandardScaler() 0 -> 0.5502704164164826
 1904/3780 0.250 1.126 1.0 MinMaxScaler()   0 -> 0.5390490328914512
 1905/3780 0.250 1.429 scale StandardScaler() 0 -> 0.5378580857154139
 1906/3780 0.250 1.429 scale MinMaxScaler()   0 -> 0.5358407272062143
 1907/3780 0.250 1.429 auto StandardScaler() 0 -> 0.5378580857154135
 1908/3780 0.250 1.429 auto MinMaxScaler()   0 -> 0.5574498987448574
 1909/3780 0.250 1.429 0.01 StandardScaler() 0 -> 0.5511945661625154
 1910/3780 0.250 1.429 0.01 MinMaxScaler()   0 -> 0.5934007779371353
 1911/3780 0.250 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5401816722234134
 1912/3780 0.250 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5855342937716328
 1913/3780 0.250 1.429 0.1 StandardScaler() 0 -> 0.5369540476714696
 1914/3780 0.250 1.429 0.1 MinMaxScaler()   0 -> 0.5738230245260297
 1915/3780 0.250 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5411927449552337
 1916/3780 0.250 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.548962087084255
 1917/3780 0.250 1.429 1.0 StandardScaler() 0 -> 0.5565741302415833
 1918/3780 0.250 1.429 1.0 MinMaxScaler()   0 -> 0.5390130715676072
 1919/3780 0.250 1.814 scale StandardScaler() 0 -> 0.5390361364389089
 1920/3780 0.250 1.814 scale MinMaxScaler()   0 -> 0.5366389177836167
 1921/3780 0.250 1.814 auto StandardScaler() 0 -> 0.5390361364389095
 1922/3780 0.250 1.814 auto MinMaxScaler()   0 -> 0.5557174977894624
 1923/3780 0.250 1.814 0.01 StandardScaler() 0 -> 0.5489823957883867
 1924/3780 0.250 1.814 0.01 MinMaxScaler()   0 -> 0.5909901168893422
 1925/3780 0.250 1.814 0.03162277660168379 StandardScaler() 0 -> 0.540004210758906
 1926/3780 0.250 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5847815135683797
 1927/3780 0.250 1.814 0.1 StandardScaler() 0 -> 0.537098326585188
 1928/3780 0.250 1.814 0.1 MinMaxScaler()   0 -> 0.5711223684642387
 1929/3780 0.250 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5422761138619987
 1930/3780 0.250 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5466384342608973
 1931/3780 0.250 1.814 1.0 StandardScaler() 0 -> 0.562815066155004
 1932/3780 0.250 1.814 1.0 MinMaxScaler()   0 -> 0.538976938161119
 1933/3780 0.250 2.302 scale StandardScaler() 0 -> 0.5407594594235735
 1934/3780 0.250 2.302 scale MinMaxScaler()   0 -> 0.5380160380022353
 1935/3780 0.250 2.302 auto StandardScaler() 0 -> 0.5407594594235742
 1936/3780 0.250 2.302 auto MinMaxScaler()   0 -> 0.5531299776904571
 1937/3780 0.250 2.302 0.01 StandardScaler() 0 -> 0.5471647958605906
 1938/3780 0.250 2.302 0.01 MinMaxScaler()   0 -> 0.5892291387775207
 1939/3780 0.250 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5403478705134268
 1940/3780 0.250 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5841108895509205
 1941/3780 0.250 2.302 0.1 StandardScaler() 0 -> 0.537115596777375
 1942/3780 0.250 2.302 0.1 MinMaxScaler()   0 -> 0.5683696210115452
 1943/3780 0.250 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5455268871671789
 1944/3780 0.250 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5454887554717218
 1945/3780 0.250 2.302 1.0 StandardScaler() 0 -> 0.5687847999150032
 1946/3780 0.250 2.302 1.0 MinMaxScaler()   0 -> 0.5391529619957481
 1947/3780 0.250 2.921 scale StandardScaler() 0 -> 0.5434126338063802
 1948/3780 0.250 2.921 scale MinMaxScaler()   0 -> 0.5399585474005704
 1949/3780 0.250 2.921 auto StandardScaler() 0 -> 0.5434126338063788
 1950/3780 0.250 2.921 auto MinMaxScaler()   0 -> 0.5509197902598993
 1951/3780 0.250 2.921 0.01 StandardScaler() 0 -> 0.5458434640883364
 1952/3780 0.250 2.921 0.01 MinMaxScaler()   0 -> 0.588295374151353
 1953/3780 0.250 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5405030770752236
 1954/3780 0.250 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5835354999008978
 1955/3780 0.250 2.921 0.1 StandardScaler() 0 -> 0.5377546950373135
 1956/3780 0.250 2.921 0.1 MinMaxScaler()   0 -> 0.5658230713626647
 1957/3780 0.250 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5496574045932877
 1958/3780 0.250 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5447618744176518
 1959/3780 0.250 2.921 1.0 StandardScaler() 0 -> 0.5771113478534631
 1960/3780 0.250 2.921 1.0 MinMaxScaler()   0 -> 0.5388316649451717
 1961/3780 0.250 3.707 scale StandardScaler() 0 -> 0.5451292647506446
 1962/3780 0.250 3.707 scale MinMaxScaler()   0 -> 0.5420271304286128
 1963/3780 0.250 3.707 auto StandardScaler() 0 -> 0.5451292647506448
 1964/3780 0.250 3.707 auto MinMaxScaler()   0 -> 0.5489397503326442
 1965/3780 0.250 3.707 0.01 StandardScaler() 0 -> 0.5449173232455001
 1966/3780 0.250 3.707 0.01 MinMaxScaler()   0 -> 0.5872090648490492
 1967/3780 0.250 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5399611269480452
 1968/3780 0.250 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5827330298767431
 1969/3780 0.250 3.707 0.1 StandardScaler() 0 -> 0.538939936637755
 1970/3780 0.250 3.707 0.1 MinMaxScaler()   0 -> 0.5623269634765835
 1971/3780 0.250 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5548647087998213
 1972/3780 0.250 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5433341287517666
 1973/3780 0.250 3.707 1.0 StandardScaler() 0 -> 0.5862582671117688
 1974/3780 0.250 3.707 1.0 MinMaxScaler()   0 -> 0.5382250551712743
 1975/3780 0.250 4.703 scale StandardScaler() 0 -> 0.5468637548292224
 1976/3780 0.250 4.703 scale MinMaxScaler()   0 -> 0.5440672250644405
 1977/3780 0.250 4.703 auto StandardScaler() 0 -> 0.5468637548292228
 1978/3780 0.250 4.703 auto MinMaxScaler()   0 -> 0.546824990950726
 1979/3780 0.250 4.703 0.01 StandardScaler() 0 -> 0.5442658342921846
 1980/3780 0.250 4.703 0.01 MinMaxScaler()   0 -> 0.5864213711874653
 1981/3780 0.250 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5395024576133193
 1982/3780 0.250 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5816988390497623
 1983/3780 0.250 4.703 0.1 StandardScaler() 0 -> 0.5398639255287722
 1984/3780 0.250 4.703 0.1 MinMaxScaler()   0 -> 0.5596691520045137
 1985/3780 0.250 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5602547491142919
 1986/3780 0.250 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5427882131468741
 1987/3780 0.250 4.703 1.0 StandardScaler() 0 -> 0.5977144140750036
 1988/3780 0.250 4.703 1.0 MinMaxScaler()   0 -> 0.5378122474581996
 1989/3780 0.250 5.968 scale StandardScaler() 0 -> 0.5502254852904788
 1990/3780 0.250 5.968 scale MinMaxScaler()   0 -> 0.546977201755155
 1991/3780 0.250 5.968 auto StandardScaler() 0 -> 0.5502254852904782
 1992/3780 0.250 5.968 auto MinMaxScaler()   0 -> 0.5459327587821704
 1993/3780 0.250 5.968 0.01 StandardScaler() 0 -> 0.5439185494728623
 1994/3780 0.250 5.968 0.01 MinMaxScaler()   0 -> 0.5861073166358782
 1995/3780 0.250 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5389841710635912
 1996/3780 0.250 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5805587674054643
 1997/3780 0.250 5.968 0.1 StandardScaler() 0 -> 0.5410782568066869
 1998/3780 0.250 5.968 0.1 MinMaxScaler()   0 -> 0.556923836468525
 1999/3780 0.250 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5666218321051892
 2000/3780 0.250 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5424919890674413
 2001/3780 0.250 5.968 1.0 StandardScaler() 0 -> 0.6098478196070268
 2002/3780 0.250 5.968 1.0 MinMaxScaler()   0 -> 0.5373256352120831
 2003/3780 0.250 7.574 scale StandardScaler() 0 -> 0.5540547575805518
 2004/3780 0.250 7.574 scale MinMaxScaler()   0 -> 0.5499244581159116
 2005/3780 0.250 7.574 auto StandardScaler() 0 -> 0.5540547575805537
 2006/3780 0.250 7.574 auto MinMaxScaler()   0 -> 0.5449586646369874
 2007/3780 0.250 7.574 0.01 StandardScaler() 0 -> 0.5435922571432665
 2008/3780 0.250 7.574 0.01 MinMaxScaler()   0 -> 0.5860159824043087
 2009/3780 0.250 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5380003298525925
 2010/3780 0.250 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5791728193374722
 2011/3780 0.250 7.574 0.1 StandardScaler() 0 -> 0.5424824015088281
 2012/3780 0.250 7.574 0.1 MinMaxScaler()   0 -> 0.5552638089108631
 2013/3780 0.250 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5734772739583064
 2014/3780 0.250 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5423026401422563
 2015/3780 0.250 7.574 1.0 StandardScaler() 0 -> 0.6255048984216276
 2016/3780 0.250 7.574 1.0 MinMaxScaler()   0 -> 0.537169649310072
 2017/3780 0.250 9.611 scale StandardScaler() 0 -> 0.5587526946808478
 2018/3780 0.250 9.611 scale MinMaxScaler()   0 -> 0.5549214848774415
 2019/3780 0.250 9.611 auto StandardScaler() 0 -> 0.5587526946808398
 2020/3780 0.250 9.611 auto MinMaxScaler()   0 -> 0.5440375868831584
 2021/3780 0.250 9.611 0.01 StandardScaler() 0 -> 0.5434877074958623
 2022/3780 0.250 9.611 0.01 MinMaxScaler()   0 -> 0.5859366376888065
 2023/3780 0.250 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5377400151645523
 2024/3780 0.250 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5774735304431625
 2025/3780 0.250 9.611 0.1 StandardScaler() 0 -> 0.5431600819493602
 2026/3780 0.250 9.611 0.1 MinMaxScaler()   0 -> 0.5527521759479831
 2027/3780 0.250 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5800858248609481
 2028/3780 0.250 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5426947972660714
 2029/3780 0.250 9.611 1.0 StandardScaler() 0 -> 0.6416358464141255
 2030/3780 0.250 9.611 1.0 MinMaxScaler()   0 -> 0.5374656007791979
 2031/3780 0.250 12.196 scale StandardScaler() 0 -> 0.5647592938607408
 2032/3780 0.250 12.196 scale MinMaxScaler()   0 -> 0.5587259118330692
 2033/3780 0.250 12.196 auto StandardScaler() 0 -> 0.5647592938607388
 2034/3780 0.250 12.196 auto MinMaxScaler()   0 -> 0.5437338966516195
 2035/3780 0.250 12.196 0.01 StandardScaler() 0 -> 0.5436022995558143
 2036/3780 0.250 12.196 0.01 MinMaxScaler()   0 -> 0.5856379838792773
 2037/3780 0.250 12.196 0.03162277660168379 StandardScaler() 0 -> 0.537827934695947
 2038/3780 0.250 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.575363656104657
 2039/3780 0.250 12.196 0.1 StandardScaler() 0 -> 0.5442086261676319
 2040/3780 0.250 12.196 0.1 MinMaxScaler()   0 -> 0.5504207028430225
 2041/3780 0.250 12.196 0.31622776601683794 StandardScaler() 0 -> 0.58732556032245
 2042/3780 0.250 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5426313768177319
 2043/3780 0.250 12.196 1.0 StandardScaler() 0 -> 0.659793203791264
 2044/3780 0.250 12.196 1.0 MinMaxScaler()   0 -> 0.5377475414430247
 2045/3780 0.250 15.476 scale StandardScaler() 0 -> 0.5708126669448997
 2046/3780 0.250 15.476 scale MinMaxScaler()   0 -> 0.5623172184861297
 2047/3780 0.250 15.476 auto StandardScaler() 0 -> 0.5708126669449035
 2048/3780 0.250 15.476 auto MinMaxScaler()   0 -> 0.5436319448476703
 2049/3780 0.250 15.476 0.01 StandardScaler() 0 -> 0.5434161749637677
 2050/3780 0.250 15.476 0.01 MinMaxScaler()   0 -> 0.5852587401705015
 2051/3780 0.250 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5378590596084788
 2052/3780 0.250 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5729492614038937
 2053/3780 0.250 15.476 0.1 StandardScaler() 0 -> 0.5457800423458804
 2054/3780 0.250 15.476 0.1 MinMaxScaler()   0 -> 0.5485543228324081
 2055/3780 0.250 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5971663957802003
 2056/3780 0.250 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5422742917082655
 2057/3780 0.250 15.476 1.0 StandardScaler() 0 -> 0.6787597740788921
 2058/3780 0.250 15.476 1.0 MinMaxScaler()   0 -> 0.5379115817278662
 2059/3780 0.250 19.638 scale StandardScaler() 0 -> 0.577735208390045
 2060/3780 0.250 19.638 scale MinMaxScaler()   0 -> 0.5666081955580776
 2061/3780 0.250 19.638 auto StandardScaler() 0 -> 0.5777352083900356
 2062/3780 0.250 19.638 auto MinMaxScaler()   0 -> 0.5435430456329375
 2063/3780 0.250 19.638 0.01 StandardScaler() 0 -> 0.5432133411366117
 2064/3780 0.250 19.638 0.01 MinMaxScaler()   0 -> 0.5848798937011729
 2065/3780 0.250 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5378426132440365
 2066/3780 0.250 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.570143252664106
 2067/3780 0.250 19.638 0.1 StandardScaler() 0 -> 0.5471735378380456
 2068/3780 0.250 19.638 0.1 MinMaxScaler()   0 -> 0.5472010444093028
 2069/3780 0.250 19.638 0.31622776601683794 StandardScaler() 0 -> 0.607318266113866
 2070/3780 0.250 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5421068961228633
 2071/3780 0.250 19.638 1.0 StandardScaler() 0 -> 0.7004778788411236
 2072/3780 0.250 19.638 1.0 MinMaxScaler()   0 -> 0.538183672075114
 2073/3780 0.250 24.920 scale StandardScaler() 0 -> 0.585676041736221
 2074/3780 0.250 24.920 scale MinMaxScaler()   0 -> 0.5716483409184038
 2075/3780 0.250 24.920 auto StandardScaler() 0 -> 0.5856760417362087
 2076/3780 0.250 24.920 auto MinMaxScaler()   0 -> 0.543434780138853
 2077/3780 0.250 24.920 0.01 StandardScaler() 0 -> 0.543005382537647
 2078/3780 0.250 24.920 0.01 MinMaxScaler()   0 -> 0.5844360678943475
 2079/3780 0.250 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5380837457335873
 2080/3780 0.250 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5675189750145245
 2081/3780 0.250 24.920 0.1 StandardScaler() 0 -> 0.5498093321169667
 2082/3780 0.250 24.920 0.1 MinMaxScaler()   0 -> 0.5462393187542495
 2083/3780 0.250 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6195893327467644
 2084/3780 0.250 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5421382010934915
 2085/3780 0.250 24.920 1.0 StandardScaler() 0 -> 0.724517259176638
 2086/3780 0.250 24.920 1.0 MinMaxScaler()   0 -> 0.5384036423158166
 2087/3780 0.250 31.623 scale StandardScaler() 0 -> 0.5935883823117728
 2088/3780 0.250 31.623 scale MinMaxScaler()   0 -> 0.576976107915287
 2089/3780 0.250 31.623 auto StandardScaler() 0 -> 0.593588382311763
 2090/3780 0.250 31.623 auto MinMaxScaler()   0 -> 0.5436106559523989
 2091/3780 0.250 31.623 0.01 StandardScaler() 0 -> 0.5427211973348612
 2092/3780 0.250 31.623 0.01 MinMaxScaler()   0 -> 0.583803227845561
 2093/3780 0.250 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5380880920185614
 2094/3780 0.250 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5643218509413676
 2095/3780 0.250 31.623 0.1 StandardScaler() 0 -> 0.5531166700029755
 2096/3780 0.250 31.623 0.1 MinMaxScaler()   0 -> 0.5453958592860715
 2097/3780 0.250 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6336087204998736
 2098/3780 0.250 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5421752704991093
 2099/3780 0.250 31.623 1.0 StandardScaler() 0 -> 0.751159341402342
 2100/3780 0.250 31.623 1.0 MinMaxScaler()   0 -> 0.538495152904899
 2101/3780 0.300 0.032 scale StandardScaler() 0 -> 0.5608376301288184
 2102/3780 0.300 0.032 scale MinMaxScaler()   0 -> 0.5574309793205027
 2103/3780 0.300 0.032 auto StandardScaler() 0 -> 0.5608376301288184
 2104/3780 0.300 0.032 auto MinMaxScaler()   0 -> 0.6101176698981142
 2105/3780 0.300 0.032 0.01 StandardScaler() 0 -> 0.60794049387996
 2106/3780 0.300 0.032 0.01 MinMaxScaler()   0 -> 0.7823076186465361
 2107/3780 0.300 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5783316460039797
 2108/3780 0.300 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7447888196586749
 2109/3780 0.300 0.032 0.1 StandardScaler() 0 -> 0.5601985695395215
 2110/3780 0.300 0.032 0.1 MinMaxScaler()   0 -> 0.6566734098618912
 2111/3780 0.300 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5671745779269951
 2112/3780 0.300 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.59669248317067
 2113/3780 0.300 0.032 1.0 StandardScaler() 0 -> 0.6252051846106939
 2114/3780 0.300 0.032 1.0 MinMaxScaler()   0 -> 0.5723187665751858
 2115/3780 0.300 0.040 scale StandardScaler() 0 -> 0.5568572050034785
 2116/3780 0.300 0.040 scale MinMaxScaler()   0 -> 0.5542241391060497
 2117/3780 0.300 0.040 auto StandardScaler() 0 -> 0.5568572050034785
 2118/3780 0.300 0.040 auto MinMaxScaler()   0 -> 0.6027900617726819
 2119/3780 0.300 0.040 0.01 StandardScaler() 0 -> 0.6003528050385256
 2120/3780 0.300 0.040 0.01 MinMaxScaler()   0 -> 0.7773434383176533
 2121/3780 0.300 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5737832217951249
 2122/3780 0.300 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.731103517056131
 2123/3780 0.300 0.040 0.1 StandardScaler() 0 -> 0.5563715402980991
 2124/3780 0.300 0.040 0.1 MinMaxScaler()   0 -> 0.6339811592112617
 2125/3780 0.300 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5621408975678167
 2126/3780 0.300 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5905370931064028
 2127/3780 0.300 0.040 1.0 StandardScaler() 0 -> 0.609856101870523
 2128/3780 0.300 0.040 1.0 MinMaxScaler()   0 -> 0.5687642798424135
 2129/3780 0.300 0.051 scale StandardScaler() 0 -> 0.5530053200247075
 2130/3780 0.300 0.051 scale MinMaxScaler()   0 -> 0.5517061447709156
 2131/3780 0.300 0.051 auto StandardScaler() 0 -> 0.5530053200247075
 2132/3780 0.300 0.051 auto MinMaxScaler()   0 -> 0.5959115707390481
 2133/3780 0.300 0.051 0.01 StandardScaler() 0 -> 0.5936595419660181
 2134/3780 0.300 0.051 0.01 MinMaxScaler()   0 -> 0.7711428054616767
 2135/3780 0.300 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5703571498397746
 2136/3780 0.300 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7147667971961278
 2137/3780 0.300 0.051 0.1 StandardScaler() 0 -> 0.5527741515575221
 2138/3780 0.300 0.051 0.1 MinMaxScaler()   0 -> 0.6188266042202838
 2139/3780 0.300 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5574309033478975
 2140/3780 0.300 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5868885329412369
 2141/3780 0.300 0.051 1.0 StandardScaler() 0 -> 0.5954788711636632
 2142/3780 0.300 0.051 1.0 MinMaxScaler()   0 -> 0.5656418147221937
 2143/3780 0.300 0.065 scale StandardScaler() 0 -> 0.5500861441930178
 2144/3780 0.300 0.065 scale MinMaxScaler()   0 -> 0.5490497770465526
 2145/3780 0.300 0.065 auto StandardScaler() 0 -> 0.5500861441930178
 2146/3780 0.300 0.065 auto MinMaxScaler()   0 -> 0.5909519016290731
 2147/3780 0.300 0.065 0.01 StandardScaler() 0 -> 0.5885743717204773
 2148/3780 0.300 0.065 0.01 MinMaxScaler()   0 -> 0.7634375511404716
 2149/3780 0.300 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5666447300161939
 2150/3780 0.300 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6955990977378156
 2151/3780 0.300 0.065 0.1 StandardScaler() 0 -> 0.5491697432740229
 2152/3780 0.300 0.065 0.1 MinMaxScaler()   0 -> 0.6085874740334405
 2153/3780 0.300 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5532048032621784
 2154/3780 0.300 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5835517097629415
 2155/3780 0.300 0.065 1.0 StandardScaler() 0 -> 0.5843776802376937
 2156/3780 0.300 0.065 1.0 MinMaxScaler()   0 -> 0.5625740349248561
 2157/3780 0.300 0.082 scale StandardScaler() 0 -> 0.5475308368246908
 2158/3780 0.300 0.082 scale MinMaxScaler()   0 -> 0.546547013614812
 2159/3780 0.300 0.082 auto StandardScaler() 0 -> 0.5475308368246908
 2160/3780 0.300 0.082 auto MinMaxScaler()   0 -> 0.5876205631128105
 2161/3780 0.300 0.082 0.01 StandardScaler() 0 -> 0.5853151975943218
 2162/3780 0.300 0.082 0.01 MinMaxScaler()   0 -> 0.753906451456476
 2163/3780 0.300 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5631074827744779
 2164/3780 0.300 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6738067833631242
 2165/3780 0.300 0.082 0.1 StandardScaler() 0 -> 0.5465361498889812
 2166/3780 0.300 0.082 0.1 MinMaxScaler()   0 -> 0.6018476889690562
 2167/3780 0.300 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5497495296508031
 2168/3780 0.300 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5807050733955846
 2169/3780 0.300 0.082 1.0 StandardScaler() 0 -> 0.5744286772328543
 2170/3780 0.300 0.082 1.0 MinMaxScaler()   0 -> 0.5595298966969996
 2171/3780 0.300 0.104 scale StandardScaler() 0 -> 0.5451363688141946
 2172/3780 0.300 0.104 scale MinMaxScaler()   0 -> 0.5444416033087278
 2173/3780 0.300 0.104 auto StandardScaler() 0 -> 0.5451363688141946
 2174/3780 0.300 0.104 auto MinMaxScaler()   0 -> 0.5849697025476409
 2175/3780 0.300 0.104 0.01 StandardScaler() 0 -> 0.5823179793544454
 2176/3780 0.300 0.104 0.01 MinMaxScaler()   0 -> 0.742229297612195
 2177/3780 0.300 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5597443267835361
 2178/3780 0.300 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6505126312504657
 2179/3780 0.300 0.104 0.1 StandardScaler() 0 -> 0.5449560488430624
 2180/3780 0.300 0.104 0.1 MinMaxScaler()   0 -> 0.5953835355512281
 2181/3780 0.300 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5470400118056061
 2182/3780 0.300 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5782189654882489
 2183/3780 0.300 0.104 1.0 StandardScaler() 0 -> 0.5662531277279478
 2184/3780 0.300 0.104 1.0 MinMaxScaler()   0 -> 0.556129338306388
 2185/3780 0.300 0.132 scale StandardScaler() 0 -> 0.5434094284774228
 2186/3780 0.300 0.132 scale MinMaxScaler()   0 -> 0.54267857449125
 2187/3780 0.300 0.132 auto StandardScaler() 0 -> 0.5434094284774226
 2188/3780 0.300 0.132 auto MinMaxScaler()   0 -> 0.582432723016561
 2189/3780 0.300 0.132 0.01 StandardScaler() 0 -> 0.5794347251981351
 2190/3780 0.300 0.132 0.01 MinMaxScaler()   0 -> 0.7280814056557045
 2191/3780 0.300 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5563584401175022
 2192/3780 0.300 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6290060671153445
 2193/3780 0.300 0.132 0.1 StandardScaler() 0 -> 0.5436633727713157
 2194/3780 0.300 0.132 0.1 MinMaxScaler()   0 -> 0.5917340266402383
 2195/3780 0.300 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5447339200895561
 2196/3780 0.300 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5755071922109686
 2197/3780 0.300 0.132 1.0 StandardScaler() 0 -> 0.5592277194397405
 2198/3780 0.300 0.132 1.0 MinMaxScaler()   0 -> 0.5532828704455769
 2199/3780 0.300 0.168 scale StandardScaler() 0 -> 0.5419231475192371
 2200/3780 0.300 0.168 scale MinMaxScaler()   0 -> 0.5410503697340023
 2201/3780 0.300 0.168 auto StandardScaler() 0 -> 0.541923147519237
 2202/3780 0.300 0.168 auto MinMaxScaler()   0 -> 0.5802820499856568
 2203/3780 0.300 0.168 0.01 StandardScaler() 0 -> 0.5767780818727148
 2204/3780 0.300 0.168 0.01 MinMaxScaler()   0 -> 0.7111893137840971
 2205/3780 0.300 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5535332471030512
 2206/3780 0.300 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6156830312552392
 2207/3780 0.300 0.168 0.1 StandardScaler() 0 -> 0.5419945571298104
 2208/3780 0.300 0.168 0.1 MinMaxScaler()   0 -> 0.5887706666981657
 2209/3780 0.300 0.168 0.31622776601683794 StandardScaler() 0 -> 0.542199380680423
 2210/3780 0.300 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.573333782030519
 2211/3780 0.300 0.168 1.0 StandardScaler() 0 -> 0.55417418077668
 2212/3780 0.300 0.168 1.0 MinMaxScaler()   0 -> 0.5505397125238883
 2213/3780 0.300 0.213 scale StandardScaler() 0 -> 0.5399513759175609
 2214/3780 0.300 0.213 scale MinMaxScaler()   0 -> 0.5398389916205469
 2215/3780 0.300 0.213 auto StandardScaler() 0 -> 0.5399513759175608
 2216/3780 0.300 0.213 auto MinMaxScaler()   0 -> 0.5783650978589248
 2217/3780 0.300 0.213 0.01 StandardScaler() 0 -> 0.5744841636088679
 2218/3780 0.300 0.213 0.01 MinMaxScaler()   0 -> 0.6914138230624151
 2219/3780 0.300 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5514609882449953
 2220/3780 0.300 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6069000543439179
 2221/3780 0.300 0.213 0.1 StandardScaler() 0 -> 0.5406011933705457
 2222/3780 0.300 0.213 0.1 MinMaxScaler()   0 -> 0.5861853783333354
 2223/3780 0.300 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5401143890691756
 2224/3780 0.300 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5710723938132933
 2225/3780 0.300 0.213 1.0 StandardScaler() 0 -> 0.5490850443574739
 2226/3780 0.300 0.213 1.0 MinMaxScaler()   0 -> 0.548366817601929
 2227/3780 0.300 0.270 scale StandardScaler() 0 -> 0.5391537961358572
 2228/3780 0.300 0.270 scale MinMaxScaler()   0 -> 0.5385091735998232
 2229/3780 0.300 0.270 auto StandardScaler() 0 -> 0.5391537961358571
 2230/3780 0.300 0.270 auto MinMaxScaler()   0 -> 0.5763650299540197
 2231/3780 0.300 0.270 0.01 StandardScaler() 0 -> 0.5718171382923617
 2232/3780 0.300 0.270 0.01 MinMaxScaler()   0 -> 0.6691578158109882
 2233/3780 0.300 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5492081702712152
 2234/3780 0.300 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6004955817975121
 2235/3780 0.300 0.270 0.1 StandardScaler() 0 -> 0.5400025258817165
 2236/3780 0.300 0.270 0.1 MinMaxScaler()   0 -> 0.5845040101474099
 2237/3780 0.300 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5387511493262012
 2238/3780 0.300 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5683306114754655
 2239/3780 0.300 0.270 1.0 StandardScaler() 0 -> 0.5448563815941313
 2240/3780 0.300 0.270 1.0 MinMaxScaler()   0 -> 0.5458016590994781
 2241/3780 0.300 0.342 scale StandardScaler() 0 -> 0.538015729679174
 2242/3780 0.300 0.342 scale MinMaxScaler()   0 -> 0.5376094537218433
 2243/3780 0.300 0.342 auto StandardScaler() 0 -> 0.5380157296791737
 2244/3780 0.300 0.342 auto MinMaxScaler()   0 -> 0.5743745451073718
 2245/3780 0.300 0.342 0.01 StandardScaler() 0 -> 0.5691327819768465
 2246/3780 0.300 0.342 0.01 MinMaxScaler()   0 -> 0.6457020413021355
 2247/3780 0.300 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5465156758677154
 2248/3780 0.300 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5950334581596612
 2249/3780 0.300 0.342 0.1 StandardScaler() 0 -> 0.5391321805683115
 2250/3780 0.300 0.342 0.1 MinMaxScaler()   0 -> 0.5827637930512876
 2251/3780 0.300 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5374000979793279
 2252/3780 0.300 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5653707063245051
 2253/3780 0.300 0.342 1.0 StandardScaler() 0 -> 0.5419029015014697
 2254/3780 0.300 0.342 1.0 MinMaxScaler()   0 -> 0.5442662443809447
 2255/3780 0.300 0.434 scale StandardScaler() 0 -> 0.5371338351748419
 2256/3780 0.300 0.434 scale MinMaxScaler()   0 -> 0.5370215712038967
 2257/3780 0.300 0.434 auto StandardScaler() 0 -> 0.5371338351748419
 2258/3780 0.300 0.434 auto MinMaxScaler()   0 -> 0.5721252394300821
 2259/3780 0.300 0.434 0.01 StandardScaler() 0 -> 0.5663904559137828
 2260/3780 0.300 0.434 0.01 MinMaxScaler()   0 -> 0.6256174557207805
 2261/3780 0.300 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5447239881983409
 2262/3780 0.300 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5920425665624379
 2263/3780 0.300 0.434 0.1 StandardScaler() 0 -> 0.5380051807565451
 2264/3780 0.300 0.434 0.1 MinMaxScaler()   0 -> 0.5813834050587157
 2265/3780 0.300 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5365510685872235
 2266/3780 0.300 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5624058672988669
 2267/3780 0.300 0.434 1.0 StandardScaler() 0 -> 0.5402715845489299
 2268/3780 0.300 0.434 1.0 MinMaxScaler()   0 -> 0.5427032845248013
 2269/3780 0.300 0.551 scale StandardScaler() 0 -> 0.5365708958732462
 2270/3780 0.300 0.551 scale MinMaxScaler()   0 -> 0.536378369791056
 2271/3780 0.300 0.551 auto StandardScaler() 0 -> 0.536570895873246
 2272/3780 0.300 0.551 auto MinMaxScaler()   0 -> 0.5696545209062488
 2273/3780 0.300 0.551 0.01 StandardScaler() 0 -> 0.563586765048798
 2274/3780 0.300 0.551 0.01 MinMaxScaler()   0 -> 0.6136419510100996
 2275/3780 0.300 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5436146729543637
 2276/3780 0.300 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5892612098298907
 2277/3780 0.300 0.551 0.1 StandardScaler() 0 -> 0.5370311257992375
 2278/3780 0.300 0.551 0.1 MinMaxScaler()   0 -> 0.5802137870427647
 2279/3780 0.300 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5353815886930052
 2280/3780 0.300 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5599526414335553
 2281/3780 0.300 0.551 1.0 StandardScaler() 0 -> 0.5396544510596861
 2282/3780 0.300 0.551 1.0 MinMaxScaler()   0 -> 0.5412477190375364
 2283/3780 0.300 0.700 scale StandardScaler() 0 -> 0.5359433645899623
 2284/3780 0.300 0.700 scale MinMaxScaler()   0 -> 0.5355887980866195
 2285/3780 0.300 0.700 auto StandardScaler() 0 -> 0.5359433645899622
 2286/3780 0.300 0.700 auto MinMaxScaler()   0 -> 0.5670527113108754
 2287/3780 0.300 0.700 0.01 StandardScaler() 0 -> 0.5610884811532477
 2288/3780 0.300 0.700 0.01 MinMaxScaler()   0 -> 0.6056594249661132
 2289/3780 0.300 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5427834542451828
 2290/3780 0.300 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5871713703047234
 2291/3780 0.300 0.700 0.1 StandardScaler() 0 -> 0.5361799628959348
 2292/3780 0.300 0.700 0.1 MinMaxScaler()   0 -> 0.5788403318746592
 2293/3780 0.300 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5352072292593694
 2294/3780 0.300 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5579450123268485
 2295/3780 0.300 0.700 1.0 StandardScaler() 0 -> 0.5412348629041852
 2296/3780 0.300 0.700 1.0 MinMaxScaler()   0 -> 0.5406545280529165
 2297/3780 0.300 0.888 scale StandardScaler() 0 -> 0.5359725471202629
 2298/3780 0.300 0.888 scale MinMaxScaler()   0 -> 0.5348386965614967
 2299/3780 0.300 0.888 auto StandardScaler() 0 -> 0.535972547120263
 2300/3780 0.300 0.888 auto MinMaxScaler()   0 -> 0.5644037952911809
 2301/3780 0.300 0.888 0.01 StandardScaler() 0 -> 0.55882083396299
 2302/3780 0.300 0.888 0.01 MinMaxScaler()   0 -> 0.5993732805841366
 2303/3780 0.300 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5419742817675764
 2304/3780 0.300 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5861928588708615
 2305/3780 0.300 0.888 0.1 StandardScaler() 0 -> 0.53590165629728
 2306/3780 0.300 0.888 0.1 MinMaxScaler()   0 -> 0.5776120493989114
 2307/3780 0.300 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5361523493041536
 2308/3780 0.300 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.555147190701215
 2309/3780 0.300 0.888 1.0 StandardScaler() 0 -> 0.5437786105798331
 2310/3780 0.300 0.888 1.0 MinMaxScaler()   0 -> 0.5398657681436624
 2311/3780 0.300 1.126 scale StandardScaler() 0 -> 0.5363010062058086
 2312/3780 0.300 1.126 scale MinMaxScaler()   0 -> 0.5348264456085229
 2313/3780 0.300 1.126 auto StandardScaler() 0 -> 0.5363010062058077
 2314/3780 0.300 1.126 auto MinMaxScaler()   0 -> 0.5614745944215537
 2315/3780 0.300 1.126 0.01 StandardScaler() 0 -> 0.5558623594913584
 2316/3780 0.300 1.126 0.01 MinMaxScaler()   0 -> 0.5944436150797286
 2317/3780 0.300 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5412427264036227
 2318/3780 0.300 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5851844665799667
 2319/3780 0.300 1.126 0.1 StandardScaler() 0 -> 0.5361254555472335
 2320/3780 0.300 1.126 0.1 MinMaxScaler()   0 -> 0.5758605710374173
 2321/3780 0.300 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5381044720206659
 2322/3780 0.300 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5528854032873607
 2323/3780 0.300 1.126 1.0 StandardScaler() 0 -> 0.5473842264982315
 2324/3780 0.300 1.126 1.0 MinMaxScaler()   0 -> 0.5393256959583658
 2325/3780 0.300 1.429 scale StandardScaler() 0 -> 0.5367546359368576
 2326/3780 0.300 1.429 scale MinMaxScaler()   0 -> 0.535025556158603
 2327/3780 0.300 1.429 auto StandardScaler() 0 -> 0.5367546359368569
 2328/3780 0.300 1.429 auto MinMaxScaler()   0 -> 0.5596017763039761
 2329/3780 0.300 1.429 0.01 StandardScaler() 0 -> 0.5530348617099087
 2330/3780 0.300 1.429 0.01 MinMaxScaler()   0 -> 0.591926591981908
 2331/3780 0.300 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5404970227200687
 2332/3780 0.300 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5840560362651943
 2333/3780 0.300 1.429 0.1 StandardScaler() 0 -> 0.5359972116366937
 2334/3780 0.300 1.429 0.1 MinMaxScaler()   0 -> 0.5737840190280226
 2335/3780 0.300 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5402215064238953
 2336/3780 0.300 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.550525109503558
 2337/3780 0.300 1.429 1.0 StandardScaler() 0 -> 0.5519205206259022
 2338/3780 0.300 1.429 1.0 MinMaxScaler()   0 -> 0.5383705148595843
 2339/3780 0.300 1.814 scale StandardScaler() 0 -> 0.5377592675101556
 2340/3780 0.300 1.814 scale MinMaxScaler()   0 -> 0.5355850900110869
 2341/3780 0.300 1.814 auto StandardScaler() 0 -> 0.5377592675101561
 2342/3780 0.300 1.814 auto MinMaxScaler()   0 -> 0.5572903037327381
 2343/3780 0.300 1.814 0.01 StandardScaler() 0 -> 0.5506796426109201
 2344/3780 0.300 1.814 0.01 MinMaxScaler()   0 -> 0.5892605404295067
 2345/3780 0.300 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5396978765814225
 2346/3780 0.300 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5835858990408067
 2347/3780 0.300 1.814 0.1 StandardScaler() 0 -> 0.5360197370231434
 2348/3780 0.300 1.814 0.1 MinMaxScaler()   0 -> 0.5713625096227872
 2349/3780 0.300 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5421538922623877
 2350/3780 0.300 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5484236321402939
 2351/3780 0.300 1.814 1.0 StandardScaler() 0 -> 0.5570954125402067
 2352/3780 0.300 1.814 1.0 MinMaxScaler()   0 -> 0.5376940639865551
 2353/3780 0.300 2.302 scale StandardScaler() 0 -> 0.5395445238706884
 2354/3780 0.300 2.302 scale MinMaxScaler()   0 -> 0.5367447051508706
 2355/3780 0.300 2.302 auto StandardScaler() 0 -> 0.539544523870689
 2356/3780 0.300 2.302 auto MinMaxScaler()   0 -> 0.5545683192842406
 2357/3780 0.300 2.302 0.01 StandardScaler() 0 -> 0.5488029152363685
 2358/3780 0.300 2.302 0.01 MinMaxScaler()   0 -> 0.5875202955762789
 2359/3780 0.300 2.302 0.03162277660168379 StandardScaler() 0 -> 0.539232883642354
 2360/3780 0.300 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5832685778669482
 2361/3780 0.300 2.302 0.1 StandardScaler() 0 -> 0.5372736758186726
 2362/3780 0.300 2.302 0.1 MinMaxScaler()   0 -> 0.5690297754851837
 2363/3780 0.300 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5451727983837876
 2364/3780 0.300 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5470453489236213
 2365/3780 0.300 2.302 1.0 StandardScaler() 0 -> 0.5623050196058297
 2366/3780 0.300 2.302 1.0 MinMaxScaler()   0 -> 0.5375237351937137
 2367/3780 0.300 2.921 scale StandardScaler() 0 -> 0.5418249188296489
 2368/3780 0.300 2.921 scale MinMaxScaler()   0 -> 0.538703830937008
 2369/3780 0.300 2.921 auto StandardScaler() 0 -> 0.5418249188296471
 2370/3780 0.300 2.921 auto MinMaxScaler()   0 -> 0.5525467774619931
 2371/3780 0.300 2.921 0.01 StandardScaler() 0 -> 0.5474807478437316
 2372/3780 0.300 2.921 0.01 MinMaxScaler()   0 -> 0.5865868570104485
 2373/3780 0.300 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5387791304140812
 2374/3780 0.300 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5825595315218717
 2375/3780 0.300 2.921 0.1 StandardScaler() 0 -> 0.5379247923064122
 2376/3780 0.300 2.921 0.1 MinMaxScaler()   0 -> 0.5662890964476897
 2377/3780 0.300 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5495324566137921
 2378/3780 0.300 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5459339931455163
 2379/3780 0.300 2.921 1.0 StandardScaler() 0 -> 0.568481370134252
 2380/3780 0.300 2.921 1.0 MinMaxScaler()   0 -> 0.5372126713241411
 2381/3780 0.300 3.707 scale StandardScaler() 0 -> 0.5436092969644933
 2382/3780 0.300 3.707 scale MinMaxScaler()   0 -> 0.5409964079779896
 2383/3780 0.300 3.707 auto StandardScaler() 0 -> 0.543609296964494
 2384/3780 0.300 3.707 auto MinMaxScaler()   0 -> 0.5505679067567172
 2385/3780 0.300 3.707 0.01 StandardScaler() 0 -> 0.5464648994566829
 2386/3780 0.300 3.707 0.01 MinMaxScaler()   0 -> 0.5856188281613511
 2387/3780 0.300 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5386795609820146
 2388/3780 0.300 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5818365235978121
 2389/3780 0.300 3.707 0.1 StandardScaler() 0 -> 0.5387333707007572
 2390/3780 0.300 3.707 0.1 MinMaxScaler()   0 -> 0.5635084807504982
 2391/3780 0.300 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5545916949381074
 2392/3780 0.300 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5449410085258801
 2393/3780 0.300 3.707 1.0 StandardScaler() 0 -> 0.5772111015410616
 2394/3780 0.300 3.707 1.0 MinMaxScaler()   0 -> 0.5369658422606974
 2395/3780 0.300 4.703 scale StandardScaler() 0 -> 0.545969052871001
 2396/3780 0.300 4.703 scale MinMaxScaler()   0 -> 0.5430717928473024
 2397/3780 0.300 4.703 auto StandardScaler() 0 -> 0.5459690528710023
 2398/3780 0.300 4.703 auto MinMaxScaler()   0 -> 0.5486633141312544
 2399/3780 0.300 4.703 0.01 StandardScaler() 0 -> 0.5455621085015742
 2400/3780 0.300 4.703 0.01 MinMaxScaler()   0 -> 0.5852914802944164
 2401/3780 0.300 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5382185724826536
 2402/3780 0.300 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5809970225010866
 2403/3780 0.300 4.703 0.1 StandardScaler() 0 -> 0.540020898055989
 2404/3780 0.300 4.703 0.1 MinMaxScaler()   0 -> 0.5607528713624568
 2405/3780 0.300 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5584537652549885
 2406/3780 0.300 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5441574297501909
 2407/3780 0.300 4.703 1.0 StandardScaler() 0 -> 0.5894511960490137
 2408/3780 0.300 4.703 1.0 MinMaxScaler()   0 -> 0.5371307650042293
 2409/3780 0.300 5.968 scale StandardScaler() 0 -> 0.5494514180898615
 2410/3780 0.300 5.968 scale MinMaxScaler()   0 -> 0.5451769260628301
 2411/3780 0.300 5.968 auto StandardScaler() 0 -> 0.5494514180898609
 2412/3780 0.300 5.968 auto MinMaxScaler()   0 -> 0.547329407654152
 2413/3780 0.300 5.968 0.01 StandardScaler() 0 -> 0.5449728578397858
 2414/3780 0.300 5.968 0.01 MinMaxScaler()   0 -> 0.5850277071232318
 2415/3780 0.300 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5379912273083831
 2416/3780 0.300 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5801127187617521
 2417/3780 0.300 5.968 0.1 StandardScaler() 0 -> 0.5415448140292071
 2418/3780 0.300 5.968 0.1 MinMaxScaler()   0 -> 0.5589986680051807
 2419/3780 0.300 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5647865020362658
 2420/3780 0.300 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5433241745469658
 2421/3780 0.300 5.968 1.0 StandardScaler() 0 -> 0.6023702186594022
 2422/3780 0.300 5.968 1.0 MinMaxScaler()   0 -> 0.5370828906211295
 2423/3780 0.300 7.574 scale StandardScaler() 0 -> 0.5534836355120363
 2424/3780 0.300 7.574 scale MinMaxScaler()   0 -> 0.5489940094696965
 2425/3780 0.300 7.574 auto StandardScaler() 0 -> 0.5534836355120335
 2426/3780 0.300 7.574 auto MinMaxScaler()   0 -> 0.5461496085363128
 2427/3780 0.300 7.574 0.01 StandardScaler() 0 -> 0.544768470403903
 2428/3780 0.300 7.574 0.01 MinMaxScaler()   0 -> 0.585118714040194
 2429/3780 0.300 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5374206899006607
 2430/3780 0.300 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5788128937782364
 2431/3780 0.300 7.574 0.1 StandardScaler() 0 -> 0.5427195893230022
 2432/3780 0.300 7.574 0.1 MinMaxScaler()   0 -> 0.556575005213941
 2433/3780 0.300 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5714851288404942
 2434/3780 0.300 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5431335615673333
 2435/3780 0.300 7.574 1.0 StandardScaler() 0 -> 0.6179674604069646
 2436/3780 0.300 7.574 1.0 MinMaxScaler()   0 -> 0.5367991185118465
 2437/3780 0.300 9.611 scale StandardScaler() 0 -> 0.5580087910190367
 2438/3780 0.300 9.611 scale MinMaxScaler()   0 -> 0.5533263344482352
 2439/3780 0.300 9.611 auto StandardScaler() 0 -> 0.558008791019038
 2440/3780 0.300 9.611 auto MinMaxScaler()   0 -> 0.5450352349507424
 2441/3780 0.300 9.611 0.01 StandardScaler() 0 -> 0.544168319884144
 2442/3780 0.300 9.611 0.01 MinMaxScaler()   0 -> 0.5846253818876223
 2443/3780 0.300 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5369122716765081
 2444/3780 0.300 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5770110597809303
 2445/3780 0.300 9.611 0.1 StandardScaler() 0 -> 0.5435763914561286
 2446/3780 0.300 9.611 0.1 MinMaxScaler()   0 -> 0.5541338919612385
 2447/3780 0.300 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5772421966917355
 2448/3780 0.300 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5429948484281119
 2449/3780 0.300 9.611 1.0 StandardScaler() 0 -> 0.6330321902847653
 2450/3780 0.300 9.611 1.0 MinMaxScaler()   0 -> 0.5370177230415076
 2451/3780 0.300 12.196 scale StandardScaler() 0 -> 0.5635789987699518
 2452/3780 0.300 12.196 scale MinMaxScaler()   0 -> 0.5574832427488495
 2453/3780 0.300 12.196 auto StandardScaler() 0 -> 0.563578998769954
 2454/3780 0.300 12.196 auto MinMaxScaler()   0 -> 0.5442171337654184
 2455/3780 0.300 12.196 0.01 StandardScaler() 0 -> 0.5440785785097875
 2456/3780 0.300 12.196 0.01 MinMaxScaler()   0 -> 0.584329966495481
 2457/3780 0.300 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5364348405790701
 2458/3780 0.300 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5750465393444689
 2459/3780 0.300 12.196 0.1 StandardScaler() 0 -> 0.5448350899072717
 2460/3780 0.300 12.196 0.1 MinMaxScaler()   0 -> 0.5523237796229893
 2461/3780 0.300 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5845188487291968
 2462/3780 0.300 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5425624348050341
 2463/3780 0.300 12.196 1.0 StandardScaler() 0 -> 0.6478850751753962
 2464/3780 0.300 12.196 1.0 MinMaxScaler()   0 -> 0.5368795880164453
 2465/3780 0.300 15.476 scale StandardScaler() 0 -> 0.5696427614034879
 2466/3780 0.300 15.476 scale MinMaxScaler()   0 -> 0.5616701822058237
 2467/3780 0.300 15.476 auto StandardScaler() 0 -> 0.5696427614034919
 2468/3780 0.300 15.476 auto MinMaxScaler()   0 -> 0.54395751374397
 2469/3780 0.300 15.476 0.01 StandardScaler() 0 -> 0.54348569364411
 2470/3780 0.300 15.476 0.01 MinMaxScaler()   0 -> 0.5839684763823
 2471/3780 0.300 15.476 0.03162277660168379 StandardScaler() 0 -> 0.536579699990127
 2472/3780 0.300 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5730085594617789
 2473/3780 0.300 15.476 0.1 StandardScaler() 0 -> 0.5460623941243071
 2474/3780 0.300 15.476 0.1 MinMaxScaler()   0 -> 0.5500567137317994
 2475/3780 0.300 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5919177135655157
 2476/3780 0.300 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5418303530119012
 2477/3780 0.300 15.476 1.0 StandardScaler() 0 -> 0.664051850469379
 2478/3780 0.300 15.476 1.0 MinMaxScaler()   0 -> 0.5368809957641648
 2479/3780 0.300 19.638 scale StandardScaler() 0 -> 0.5768646937441136
 2480/3780 0.300 19.638 scale MinMaxScaler()   0 -> 0.5666155859953675
 2481/3780 0.300 19.638 auto StandardScaler() 0 -> 0.5768646937441
 2482/3780 0.300 19.638 auto MinMaxScaler()   0 -> 0.5438726247752507
 2483/3780 0.300 19.638 0.01 StandardScaler() 0 -> 0.5432482240359837
 2484/3780 0.300 19.638 0.01 MinMaxScaler()   0 -> 0.5836483405391317
 2485/3780 0.300 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5367859659278009
 2486/3780 0.300 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5704451401010902
 2487/3780 0.300 19.638 0.1 StandardScaler() 0 -> 0.5469220976658308
 2488/3780 0.300 19.638 0.1 MinMaxScaler()   0 -> 0.5487590922143673
 2489/3780 0.300 19.638 0.31622776601683794 StandardScaler() 0 -> 0.6013118547415434
 2490/3780 0.300 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5413847656177158
 2491/3780 0.300 19.638 1.0 StandardScaler() 0 -> 0.6835887585165272
 2492/3780 0.300 19.638 1.0 MinMaxScaler()   0 -> 0.5371456353096723
 2493/3780 0.300 24.920 scale StandardScaler() 0 -> 0.5833320352406667
 2494/3780 0.300 24.920 scale MinMaxScaler()   0 -> 0.5715202544171808
 2495/3780 0.300 24.920 auto StandardScaler() 0 -> 0.5833320352406651
 2496/3780 0.300 24.920 auto MinMaxScaler()   0 -> 0.5436872633843243
 2497/3780 0.300 24.920 0.01 StandardScaler() 0 -> 0.5427946303695975
 2498/3780 0.300 24.920 0.01 MinMaxScaler()   0 -> 0.5829912989163263
 2499/3780 0.300 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5374283467470452
 2500/3780 0.300 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.567741990213006
 2501/3780 0.300 24.920 0.1 StandardScaler() 0 -> 0.547709752665252
 2502/3780 0.300 24.920 0.1 MinMaxScaler()   0 -> 0.5472890667033762
 2503/3780 0.300 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6150134900856861
 2504/3780 0.300 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.540934566140429
 2505/3780 0.300 24.920 1.0 StandardScaler() 0 -> 0.7054221283620211
 2506/3780 0.300 24.920 1.0 MinMaxScaler()   0 -> 0.5373889669233254
 2507/3780 0.300 31.623 scale StandardScaler() 0 -> 0.5906694981153491
 2508/3780 0.300 31.623 scale MinMaxScaler()   0 -> 0.5775552772505135
 2509/3780 0.300 31.623 auto StandardScaler() 0 -> 0.5906694981153433
 2510/3780 0.300 31.623 auto MinMaxScaler()   0 -> 0.5432954374205897
 2511/3780 0.300 31.623 0.01 StandardScaler() 0 -> 0.5418439852496508
 2512/3780 0.300 31.623 0.01 MinMaxScaler()   0 -> 0.5822834466104364
 2513/3780 0.300 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5376006627341188
 2514/3780 0.300 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5650739024154742
 2515/3780 0.300 31.623 0.1 StandardScaler() 0 -> 0.5503183951058775
 2516/3780 0.300 31.623 0.1 MinMaxScaler()   0 -> 0.5463773442123085
 2517/3780 0.300 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6291918497907404
 2518/3780 0.300 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5402890542236657
 2519/3780 0.300 31.623 1.0 StandardScaler() 0 -> 0.7287347693888159
 2520/3780 0.300 31.623 1.0 MinMaxScaler()   0 -> 0.537138524299432
 2521/3780 0.350 0.032 scale StandardScaler() 0 -> 0.5625197398564179
 2522/3780 0.350 0.032 scale MinMaxScaler()   0 -> 0.5601321177874937
 2523/3780 0.350 0.032 auto StandardScaler() 0 -> 0.5625197398564179
 2524/3780 0.350 0.032 auto MinMaxScaler()   0 -> 0.6072091062235948
 2525/3780 0.350 0.032 0.01 StandardScaler() 0 -> 0.6052259917896475
 2526/3780 0.350 0.032 0.01 MinMaxScaler()   0 -> 0.8047389209521794
 2527/3780 0.350 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5781022564606279
 2528/3780 0.350 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7643217795049521
 2529/3780 0.350 0.032 0.1 StandardScaler() 0 -> 0.5623628323958368
 2530/3780 0.350 0.032 0.1 MinMaxScaler()   0 -> 0.6672910381487996
 2531/3780 0.350 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5685753442464707
 2532/3780 0.350 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5953682805158079
 2533/3780 0.350 0.032 1.0 StandardScaler() 0 -> 0.6304177782112409
 2534/3780 0.350 0.032 1.0 MinMaxScaler()   0 -> 0.573918063677833
 2535/3780 0.350 0.040 scale StandardScaler() 0 -> 0.5590391303386079
 2536/3780 0.350 0.040 scale MinMaxScaler()   0 -> 0.5574175132124143
 2537/3780 0.350 0.040 auto StandardScaler() 0 -> 0.5590391303386077
 2538/3780 0.350 0.040 auto MinMaxScaler()   0 -> 0.5995309806094867
 2539/3780 0.350 0.040 0.01 StandardScaler() 0 -> 0.5971715943413348
 2540/3780 0.350 0.040 0.01 MinMaxScaler()   0 -> 0.7994092630561166
 2541/3780 0.350 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5747850822696752
 2542/3780 0.350 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7495476692561267
 2543/3780 0.350 0.040 0.1 StandardScaler() 0 -> 0.5584022317860767
 2544/3780 0.350 0.040 0.1 MinMaxScaler()   0 -> 0.6403414637536036
 2545/3780 0.350 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5634907357263731
 2546/3780 0.350 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5896927858816698
 2547/3780 0.350 0.040 1.0 StandardScaler() 0 -> 0.6124502493157072
 2548/3780 0.350 0.040 1.0 MinMaxScaler()   0 -> 0.5710408033449146
 2549/3780 0.350 0.051 scale StandardScaler() 0 -> 0.5554470928878064
 2550/3780 0.350 0.051 scale MinMaxScaler()   0 -> 0.5551811547971242
 2551/3780 0.350 0.051 auto StandardScaler() 0 -> 0.5554470928878062
 2552/3780 0.350 0.051 auto MinMaxScaler()   0 -> 0.594718074706276
 2553/3780 0.350 0.051 0.01 StandardScaler() 0 -> 0.5926271075355551
 2554/3780 0.350 0.051 0.01 MinMaxScaler()   0 -> 0.7927494502468958
 2555/3780 0.350 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5716372496995725
 2556/3780 0.350 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.731683304475431
 2557/3780 0.350 0.051 0.1 StandardScaler() 0 -> 0.5554409323962061
 2558/3780 0.350 0.051 0.1 MinMaxScaler()   0 -> 0.6179556801363411
 2559/3780 0.350 0.051 0.31622776601683794 StandardScaler() 0 -> 0.559470658949022
 2560/3780 0.350 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5860359624245494
 2561/3780 0.350 0.051 1.0 StandardScaler() 0 -> 0.5977669047240332
 2562/3780 0.350 0.051 1.0 MinMaxScaler()   0 -> 0.5679564471434564
 2563/3780 0.350 0.065 scale StandardScaler() 0 -> 0.5526050550262601
 2564/3780 0.350 0.065 scale MinMaxScaler()   0 -> 0.5524874024402907
 2565/3780 0.350 0.065 auto StandardScaler() 0 -> 0.5526050550262602
 2566/3780 0.350 0.065 auto MinMaxScaler()   0 -> 0.5900045635778478
 2567/3780 0.350 0.065 0.01 StandardScaler() 0 -> 0.5875229062463362
 2568/3780 0.350 0.065 0.01 MinMaxScaler()   0 -> 0.7844469399636865
 2569/3780 0.350 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5686756836974838
 2570/3780 0.350 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.7106668626473877
 2571/3780 0.350 0.065 0.1 StandardScaler() 0 -> 0.5527101094986584
 2572/3780 0.350 0.065 0.1 MinMaxScaler()   0 -> 0.6058625506183274
 2573/3780 0.350 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5557786150469619
 2574/3780 0.350 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5832756449622691
 2575/3780 0.350 0.065 1.0 StandardScaler() 0 -> 0.5857201025202677
 2576/3780 0.350 0.065 1.0 MinMaxScaler()   0 -> 0.5650089399222481
 2577/3780 0.350 0.082 scale StandardScaler() 0 -> 0.5503004827252335
 2578/3780 0.350 0.082 scale MinMaxScaler()   0 -> 0.5506247990171401
 2579/3780 0.350 0.082 auto StandardScaler() 0 -> 0.5503004827252332
 2580/3780 0.350 0.082 auto MinMaxScaler()   0 -> 0.5865349290186445
 2581/3780 0.350 0.082 0.01 StandardScaler() 0 -> 0.5842907319830891
 2582/3780 0.350 0.082 0.01 MinMaxScaler()   0 -> 0.7741248250347542
 2583/3780 0.350 0.082 0.03162277660168379 StandardScaler() 0 -> 0.565669751793422
 2584/3780 0.350 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6865349488054323
 2585/3780 0.350 0.082 0.1 StandardScaler() 0 -> 0.5501902888927778
 2586/3780 0.350 0.082 0.1 MinMaxScaler()   0 -> 0.5989342263574611
 2587/3780 0.350 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5518711062546071
 2588/3780 0.350 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5810000326326407
 2589/3780 0.350 0.082 1.0 StandardScaler() 0 -> 0.5756546815276701
 2590/3780 0.350 0.082 1.0 MinMaxScaler()   0 -> 0.5621149566392419
 2591/3780 0.350 0.104 scale StandardScaler() 0 -> 0.5483613885330857
 2592/3780 0.350 0.104 scale MinMaxScaler()   0 -> 0.5485100688329484
 2593/3780 0.350 0.104 auto StandardScaler() 0 -> 0.5483613885330857
 2594/3780 0.350 0.104 auto MinMaxScaler()   0 -> 0.5843209898045197
 2595/3780 0.350 0.104 0.01 StandardScaler() 0 -> 0.5817720760543706
 2596/3780 0.350 0.104 0.01 MinMaxScaler()   0 -> 0.7615483659925433
 2597/3780 0.350 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5622805460272018
 2598/3780 0.350 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6599051733815054
 2599/3780 0.350 0.104 0.1 StandardScaler() 0 -> 0.5484648944292573
 2600/3780 0.350 0.104 0.1 MinMaxScaler()   0 -> 0.5942803363927661
 2601/3780 0.350 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5489144445055433
 2602/3780 0.350 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5789584140021362
 2603/3780 0.350 0.104 1.0 StandardScaler() 0 -> 0.5674832747254275
 2604/3780 0.350 0.104 1.0 MinMaxScaler()   0 -> 0.5594148297570444
 2605/3780 0.350 0.132 scale StandardScaler() 0 -> 0.5461152021613928
 2606/3780 0.350 0.132 scale MinMaxScaler()   0 -> 0.5463208364965753
 2607/3780 0.350 0.132 auto StandardScaler() 0 -> 0.5461152021613928
 2608/3780 0.350 0.132 auto MinMaxScaler()   0 -> 0.5825701483991589
 2609/3780 0.350 0.132 0.01 StandardScaler() 0 -> 0.5793404262130726
 2610/3780 0.350 0.132 0.01 MinMaxScaler()   0 -> 0.7462009251759775
 2611/3780 0.350 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5593663266310459
 2612/3780 0.350 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6332993101146046
 2613/3780 0.350 0.132 0.1 StandardScaler() 0 -> 0.546761857570606
 2614/3780 0.350 0.132 0.1 MinMaxScaler()   0 -> 0.590363821923722
 2615/3780 0.350 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5459421724391321
 2616/3780 0.350 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5770656115753573
 2617/3780 0.350 0.132 1.0 StandardScaler() 0 -> 0.5610872191107438
 2618/3780 0.350 0.132 1.0 MinMaxScaler()   0 -> 0.5566194418785309
 2619/3780 0.350 0.168 scale StandardScaler() 0 -> 0.5443262766923519
 2620/3780 0.350 0.168 scale MinMaxScaler()   0 -> 0.5447058305771421
 2621/3780 0.350 0.168 auto StandardScaler() 0 -> 0.5443262766923518
 2622/3780 0.350 0.168 auto MinMaxScaler()   0 -> 0.5808142807807101
 2623/3780 0.350 0.168 0.01 StandardScaler() 0 -> 0.5774900999531102
 2624/3780 0.350 0.168 0.01 MinMaxScaler()   0 -> 0.7277836543137944
 2625/3780 0.350 0.168 0.03162277660168379 StandardScaler() 0 -> 0.55702021245513
 2626/3780 0.350 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6138318645137373
 2627/3780 0.350 0.168 0.1 StandardScaler() 0 -> 0.5453590085916268
 2628/3780 0.350 0.168 0.1 MinMaxScaler()   0 -> 0.5875986027506048
 2629/3780 0.350 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5433397362458492
 2630/3780 0.350 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5746717931609769
 2631/3780 0.350 0.168 1.0 StandardScaler() 0 -> 0.5556004961209928
 2632/3780 0.350 0.168 1.0 MinMaxScaler()   0 -> 0.55414581481242
 2633/3780 0.350 0.213 scale StandardScaler() 0 -> 0.5424906190397677
 2634/3780 0.350 0.213 scale MinMaxScaler()   0 -> 0.5426811571404432
 2635/3780 0.350 0.213 auto StandardScaler() 0 -> 0.5424906190397676
 2636/3780 0.350 0.213 auto MinMaxScaler()   0 -> 0.5792287670004911
 2637/3780 0.350 0.213 0.01 StandardScaler() 0 -> 0.5753311383374093
 2638/3780 0.350 0.213 0.01 MinMaxScaler()   0 -> 0.7060561340890259
 2639/3780 0.350 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5542637306058505
 2640/3780 0.350 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6039625250056054
 2641/3780 0.350 0.213 0.1 StandardScaler() 0 -> 0.5436772972932006
 2642/3780 0.350 0.213 0.1 MinMaxScaler()   0 -> 0.5857805549879206
 2643/3780 0.350 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5416432159890233
 2644/3780 0.350 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5720941496018295
 2645/3780 0.350 0.213 1.0 StandardScaler() 0 -> 0.5499130803280664
 2646/3780 0.350 0.213 1.0 MinMaxScaler()   0 -> 0.5519867757765534
 2647/3780 0.350 0.270 scale StandardScaler() 0 -> 0.5404489483551423
 2648/3780 0.350 0.270 scale MinMaxScaler()   0 -> 0.5407686282783511
 2649/3780 0.350 0.270 auto StandardScaler() 0 -> 0.5404489483551422
 2650/3780 0.350 0.270 auto MinMaxScaler()   0 -> 0.5775374640813036
 2651/3780 0.350 0.270 0.01 StandardScaler() 0 -> 0.5730402109038021
 2652/3780 0.350 0.270 0.01 MinMaxScaler()   0 -> 0.6813397143623426
 2653/3780 0.350 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5520814112006006
 2654/3780 0.350 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5983046588839568
 2655/3780 0.350 0.270 0.1 StandardScaler() 0 -> 0.5424354098135055
 2656/3780 0.350 0.270 0.1 MinMaxScaler()   0 -> 0.5843709701986965
 2657/3780 0.350 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5399336317905656
 2658/3780 0.350 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5695514585144913
 2659/3780 0.350 0.270 1.0 StandardScaler() 0 -> 0.5457864492574448
 2660/3780 0.350 0.270 1.0 MinMaxScaler()   0 -> 0.5494818510845533
 2661/3780 0.350 0.342 scale StandardScaler() 0 -> 0.5389252446300328
 2662/3780 0.350 0.342 scale MinMaxScaler()   0 -> 0.5393510308618272
 2663/3780 0.350 0.342 auto StandardScaler() 0 -> 0.5389252446300326
 2664/3780 0.350 0.342 auto MinMaxScaler()   0 -> 0.5754385321470509
 2665/3780 0.350 0.342 0.01 StandardScaler() 0 -> 0.5707118771428648
 2666/3780 0.350 0.342 0.01 MinMaxScaler()   0 -> 0.6544058476414034
 2667/3780 0.350 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5503304265067726
 2668/3780 0.350 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5938029296850403
 2669/3780 0.350 0.342 0.1 StandardScaler() 0 -> 0.5410250448924755
 2670/3780 0.350 0.342 0.1 MinMaxScaler()   0 -> 0.5831758747034677
 2671/3780 0.350 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5384106456243923
 2672/3780 0.350 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.567330428402994
 2673/3780 0.350 0.342 1.0 StandardScaler() 0 -> 0.542933144416406
 2674/3780 0.350 0.342 1.0 MinMaxScaler()   0 -> 0.547083074052625
 2675/3780 0.350 0.434 scale StandardScaler() 0 -> 0.538225985713818
 2676/3780 0.350 0.434 scale MinMaxScaler()   0 -> 0.5391087027519289
 2677/3780 0.350 0.434 auto StandardScaler() 0 -> 0.5382259857138181
 2678/3780 0.350 0.434 auto MinMaxScaler()   0 -> 0.5732517016910569
 2679/3780 0.350 0.434 0.01 StandardScaler() 0 -> 0.5681759317076344
 2680/3780 0.350 0.434 0.01 MinMaxScaler()   0 -> 0.6286942705281771
 2681/3780 0.350 0.434 0.03162277660168379 StandardScaler() 0 -> 0.54802470142285
 2682/3780 0.350 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5902274011997645
 2683/3780 0.350 0.434 0.1 StandardScaler() 0 -> 0.539266746692914
 2684/3780 0.350 0.434 0.1 MinMaxScaler()   0 -> 0.581855487741873
 2685/3780 0.350 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5374105957289732
 2686/3780 0.350 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5651359602434999
 2687/3780 0.350 0.434 1.0 StandardScaler() 0 -> 0.5412114496339074
 2688/3780 0.350 0.434 1.0 MinMaxScaler()   0 -> 0.545690511645989
 2689/3780 0.350 0.551 scale StandardScaler() 0 -> 0.5375367684404793
 2690/3780 0.350 0.551 scale MinMaxScaler()   0 -> 0.5379737770165508
 2691/3780 0.350 0.551 auto StandardScaler() 0 -> 0.5375367684404788
 2692/3780 0.350 0.551 auto MinMaxScaler()   0 -> 0.5709853345741501
 2693/3780 0.350 0.551 0.01 StandardScaler() 0 -> 0.566037085286144
 2694/3780 0.350 0.551 0.01 MinMaxScaler()   0 -> 0.6116481395510377
 2695/3780 0.350 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5460490394137372
 2696/3780 0.350 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5883241743290409
 2697/3780 0.350 0.551 0.1 StandardScaler() 0 -> 0.538315089008937
 2698/3780 0.350 0.551 0.1 MinMaxScaler()   0 -> 0.5807735676613567
 2699/3780 0.350 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5369537774389846
 2700/3780 0.350 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5628180427756383
 2701/3780 0.350 0.551 1.0 StandardScaler() 0 -> 0.5407688565904437
 2702/3780 0.350 0.551 1.0 MinMaxScaler()   0 -> 0.5447352082897771
 2703/3780 0.350 0.700 scale StandardScaler() 0 -> 0.5368635001007216
 2704/3780 0.350 0.700 scale MinMaxScaler()   0 -> 0.5369662866434807
 2705/3780 0.350 0.700 auto StandardScaler() 0 -> 0.536863500100721
 2706/3780 0.350 0.700 auto MinMaxScaler()   0 -> 0.5686940448262545
 2707/3780 0.350 0.700 0.01 StandardScaler() 0 -> 0.5634160251721892
 2708/3780 0.350 0.700 0.01 MinMaxScaler()   0 -> 0.6027487270642853
 2709/3780 0.350 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5448893974479416
 2710/3780 0.350 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5868754446977638
 2711/3780 0.350 0.700 0.1 StandardScaler() 0 -> 0.5377622572875496
 2712/3780 0.350 0.700 0.1 MinMaxScaler()   0 -> 0.5797441184103187
 2713/3780 0.350 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5362832738097836
 2714/3780 0.350 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5603209869793324
 2715/3780 0.350 0.700 1.0 StandardScaler() 0 -> 0.5415634456522646
 2716/3780 0.350 0.700 1.0 MinMaxScaler()   0 -> 0.5435567129723676
 2717/3780 0.350 0.888 scale StandardScaler() 0 -> 0.5367293820997728
 2718/3780 0.350 0.888 scale MinMaxScaler()   0 -> 0.5361572220657627
 2719/3780 0.350 0.888 auto StandardScaler() 0 -> 0.5367293820997728
 2720/3780 0.350 0.888 auto MinMaxScaler()   0 -> 0.5663861803968858
 2721/3780 0.350 0.888 0.01 StandardScaler() 0 -> 0.5607345468705229
 2722/3780 0.350 0.888 0.01 MinMaxScaler()   0 -> 0.5976306034872264
 2723/3780 0.350 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5439856588013741
 2724/3780 0.350 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.58599070356271
 2725/3780 0.350 0.888 0.1 StandardScaler() 0 -> 0.5369966987375995
 2726/3780 0.350 0.888 0.1 MinMaxScaler()   0 -> 0.5780792120530612
 2727/3780 0.350 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5369518145993254
 2728/3780 0.350 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5582087265245735
 2729/3780 0.350 0.888 1.0 StandardScaler() 0 -> 0.5427804527243029
 2730/3780 0.350 0.888 1.0 MinMaxScaler()   0 -> 0.5428783703207459
 2731/3780 0.350 1.126 scale StandardScaler() 0 -> 0.5368814047916882
 2732/3780 0.350 1.126 scale MinMaxScaler()   0 -> 0.5359005360592328
 2733/3780 0.350 1.126 auto StandardScaler() 0 -> 0.5368814047916873
 2734/3780 0.350 1.126 auto MinMaxScaler()   0 -> 0.5644474731008856
 2735/3780 0.350 1.126 0.01 StandardScaler() 0 -> 0.5581408148791599
 2736/3780 0.350 1.126 0.01 MinMaxScaler()   0 -> 0.5932298924572749
 2737/3780 0.350 1.126 0.03162277660168379 StandardScaler() 0 -> 0.543279779106756
 2738/3780 0.350 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5851124603028799
 2739/3780 0.350 1.126 0.1 StandardScaler() 0 -> 0.5371394319079315
 2740/3780 0.350 1.126 0.1 MinMaxScaler()   0 -> 0.5762670573073456
 2741/3780 0.350 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5374269251950916
 2742/3780 0.350 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5557729501227535
 2743/3780 0.350 1.126 1.0 StandardScaler() 0 -> 0.5450591337124556
 2744/3780 0.350 1.126 1.0 MinMaxScaler()   0 -> 0.5419175777257031
 2745/3780 0.350 1.429 scale StandardScaler() 0 -> 0.5373583027545055
 2746/3780 0.350 1.429 scale MinMaxScaler()   0 -> 0.5367540121808516
 2747/3780 0.350 1.429 auto StandardScaler() 0 -> 0.5373583027545047
 2748/3780 0.350 1.429 auto MinMaxScaler()   0 -> 0.562159838269772
 2749/3780 0.350 1.429 0.01 StandardScaler() 0 -> 0.5560919982833447
 2750/3780 0.350 1.429 0.01 MinMaxScaler()   0 -> 0.5900967186050394
 2751/3780 0.350 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5423792359506439
 2752/3780 0.350 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5845048306576451
 2753/3780 0.350 1.429 0.1 StandardScaler() 0 -> 0.5364368221260888
 2754/3780 0.350 1.429 0.1 MinMaxScaler()   0 -> 0.5744918488999361
 2755/3780 0.350 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5398577862549055
 2756/3780 0.350 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5538172201794808
 2757/3780 0.350 1.429 1.0 StandardScaler() 0 -> 0.5486903052558568
 2758/3780 0.350 1.429 1.0 MinMaxScaler()   0 -> 0.5411613921874064
 2759/3780 0.350 1.814 scale StandardScaler() 0 -> 0.5386661928898091
 2760/3780 0.350 1.814 scale MinMaxScaler()   0 -> 0.5370646431008581
 2761/3780 0.350 1.814 auto StandardScaler() 0 -> 0.538666192889809
 2762/3780 0.350 1.814 auto MinMaxScaler()   0 -> 0.5596628050683273
 2763/3780 0.350 1.814 0.01 StandardScaler() 0 -> 0.5537450804424956
 2764/3780 0.350 1.814 0.01 MinMaxScaler()   0 -> 0.5884674412933942
 2765/3780 0.350 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5418326629118178
 2766/3780 0.350 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.583901078939542
 2767/3780 0.350 1.814 0.1 StandardScaler() 0 -> 0.5364898261019198
 2768/3780 0.350 1.814 0.1 MinMaxScaler()   0 -> 0.5725967906476906
 2769/3780 0.350 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5424074732722414
 2770/3780 0.350 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5515570835661006
 2771/3780 0.350 1.814 1.0 StandardScaler() 0 -> 0.5522376618394214
 2772/3780 0.350 1.814 1.0 MinMaxScaler()   0 -> 0.5406330741771911
 2773/3780 0.350 2.302 scale StandardScaler() 0 -> 0.5398276542371998
 2774/3780 0.350 2.302 scale MinMaxScaler()   0 -> 0.5375329735592933
 2775/3780 0.350 2.302 auto StandardScaler() 0 -> 0.5398276542371998
 2776/3780 0.350 2.302 auto MinMaxScaler()   0 -> 0.5574709369584773
 2777/3780 0.350 2.302 0.01 StandardScaler() 0 -> 0.551901144943529
 2778/3780 0.350 2.302 0.01 MinMaxScaler()   0 -> 0.5873765421660105
 2779/3780 0.350 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5411270886413532
 2780/3780 0.350 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5834873779428941
 2781/3780 0.350 2.302 0.1 StandardScaler() 0 -> 0.5369479013192634
 2782/3780 0.350 2.302 0.1 MinMaxScaler()   0 -> 0.5701176364890447
 2783/3780 0.350 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5450655984127036
 2784/3780 0.350 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5499998362285963
 2785/3780 0.350 2.302 1.0 StandardScaler() 0 -> 0.5565392083256916
 2786/3780 0.350 2.302 1.0 MinMaxScaler()   0 -> 0.5393553380102597
 2787/3780 0.350 2.921 scale StandardScaler() 0 -> 0.5417317814957161
 2788/3780 0.350 2.921 scale MinMaxScaler()   0 -> 0.539041617410698
 2789/3780 0.350 2.921 auto StandardScaler() 0 -> 0.5417317814957144
 2790/3780 0.350 2.921 auto MinMaxScaler()   0 -> 0.5552118319511834
 2791/3780 0.350 2.921 0.01 StandardScaler() 0 -> 0.5506604245844159
 2792/3780 0.350 2.921 0.01 MinMaxScaler()   0 -> 0.5863873030722169
 2793/3780 0.350 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5402944809407624
 2794/3780 0.350 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5828391534071266
 2795/3780 0.350 2.921 0.1 StandardScaler() 0 -> 0.5380688932502591
 2796/3780 0.350 2.921 0.1 MinMaxScaler()   0 -> 0.5678403329012359
 2797/3780 0.350 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5491896107824578
 2798/3780 0.350 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5484576730011286
 2799/3780 0.350 2.921 1.0 StandardScaler() 0 -> 0.562387501305487
 2800/3780 0.350 2.921 1.0 MinMaxScaler()   0 -> 0.538587651208733
 2801/3780 0.350 3.707 scale StandardScaler() 0 -> 0.5436232692037944
 2802/3780 0.350 3.707 scale MinMaxScaler()   0 -> 0.5412411628359028
 2803/3780 0.350 3.707 auto StandardScaler() 0 -> 0.5436232692037958
 2804/3780 0.350 3.707 auto MinMaxScaler()   0 -> 0.5532129321598847
 2805/3780 0.350 3.707 0.01 StandardScaler() 0 -> 0.5488082595407265
 2806/3780 0.350 3.707 0.01 MinMaxScaler()   0 -> 0.5858464051706158
 2807/3780 0.350 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5395972910076051
 2808/3780 0.350 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5821363226872031
 2809/3780 0.350 3.707 0.1 StandardScaler() 0 -> 0.5393684406960855
 2810/3780 0.350 3.707 0.1 MinMaxScaler()   0 -> 0.5655487445332436
 2811/3780 0.350 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5531014695001925
 2812/3780 0.350 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5478183609970446
 2813/3780 0.350 3.707 1.0 StandardScaler() 0 -> 0.5709155808705503
 2814/3780 0.350 3.707 1.0 MinMaxScaler()   0 -> 0.5377258294492177
 2815/3780 0.350 4.703 scale StandardScaler() 0 -> 0.5452091620139975
 2816/3780 0.350 4.703 scale MinMaxScaler()   0 -> 0.5431322463535726
 2817/3780 0.350 4.703 auto StandardScaler() 0 -> 0.5452091620139977
 2818/3780 0.350 4.703 auto MinMaxScaler()   0 -> 0.5514862672111279
 2819/3780 0.350 4.703 0.01 StandardScaler() 0 -> 0.5480527891010402
 2820/3780 0.350 4.703 0.01 MinMaxScaler()   0 -> 0.5853665373129519
 2821/3780 0.350 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5386955061056597
 2822/3780 0.350 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5811319166836362
 2823/3780 0.350 4.703 0.1 StandardScaler() 0 -> 0.5401469312736028
 2824/3780 0.350 4.703 0.1 MinMaxScaler()   0 -> 0.5638522047307613
 2825/3780 0.350 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5570456174482797
 2826/3780 0.350 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5467837001122776
 2827/3780 0.350 4.703 1.0 StandardScaler() 0 -> 0.5823767586321276
 2828/3780 0.350 4.703 1.0 MinMaxScaler()   0 -> 0.5371833138412035
 2829/3780 0.350 5.968 scale StandardScaler() 0 -> 0.5490113167848883
 2830/3780 0.350 5.968 scale MinMaxScaler()   0 -> 0.5454489646925028
 2831/3780 0.350 5.968 auto StandardScaler() 0 -> 0.5490113167848892
 2832/3780 0.350 5.968 auto MinMaxScaler()   0 -> 0.5503419018855388
 2833/3780 0.350 5.968 0.01 StandardScaler() 0 -> 0.5473357562955504
 2834/3780 0.350 5.968 0.01 MinMaxScaler()   0 -> 0.585202834650126
 2835/3780 0.350 5.968 0.03162277660168379 StandardScaler() 0 -> 0.5377985886746942
 2836/3780 0.350 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5799196069328744
 2837/3780 0.350 5.968 0.1 StandardScaler() 0 -> 0.5407110681975532
 2838/3780 0.350 5.968 0.1 MinMaxScaler()   0 -> 0.5615251857536109
 2839/3780 0.350 5.968 0.31622776601683794 StandardScaler() 0 -> 0.562246374887296
 2840/3780 0.350 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5459080497523688
 2841/3780 0.350 5.968 1.0 StandardScaler() 0 -> 0.5954169564158595
 2842/3780 0.350 5.968 1.0 MinMaxScaler()   0 -> 0.5370533262031615
 2843/3780 0.350 7.574 scale StandardScaler() 0 -> 0.5535134408010939
 2844/3780 0.350 7.574 scale MinMaxScaler()   0 -> 0.5497933175347407
 2845/3780 0.350 7.574 auto StandardScaler() 0 -> 0.5535134408010931
 2846/3780 0.350 7.574 auto MinMaxScaler()   0 -> 0.5488844837545341
 2847/3780 0.350 7.574 0.01 StandardScaler() 0 -> 0.5466871503929159
 2848/3780 0.350 7.574 0.01 MinMaxScaler()   0 -> 0.5850441900785112
 2849/3780 0.350 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5374544262030437
 2850/3780 0.350 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5786809706048227
 2851/3780 0.350 7.574 0.1 StandardScaler() 0 -> 0.5421149674934755
 2852/3780 0.350 7.574 0.1 MinMaxScaler()   0 -> 0.5587955366719843
 2853/3780 0.350 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5668096107090731
 2854/3780 0.350 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5452625001863717
 2855/3780 0.350 7.574 1.0 StandardScaler() 0 -> 0.6092399505082243
 2856/3780 0.350 7.574 1.0 MinMaxScaler()   0 -> 0.5368144370649744
 2857/3780 0.350 9.611 scale StandardScaler() 0 -> 0.5586140899585889
 2858/3780 0.350 9.611 scale MinMaxScaler()   0 -> 0.5543526433570679
 2859/3780 0.350 9.611 auto StandardScaler() 0 -> 0.5586140899585824
 2860/3780 0.350 9.611 auto MinMaxScaler()   0 -> 0.548098079799818
 2861/3780 0.350 9.611 0.01 StandardScaler() 0 -> 0.5462691477468374
 2862/3780 0.350 9.611 0.01 MinMaxScaler()   0 -> 0.5848440641742586
 2863/3780 0.350 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5371616336738362
 2864/3780 0.350 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.576937453951865
 2865/3780 0.350 9.611 0.1 StandardScaler() 0 -> 0.5438787474681456
 2866/3780 0.350 9.611 0.1 MinMaxScaler()   0 -> 0.5567776969729761
 2867/3780 0.350 9.611 0.31622776601683794 StandardScaler() 0 -> 0.573186236748076
 2868/3780 0.350 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5445717994711663
 2869/3780 0.350 9.611 1.0 StandardScaler() 0 -> 0.6221823057504278
 2870/3780 0.350 9.611 1.0 MinMaxScaler()   0 -> 0.5367430181717573
 2871/3780 0.350 12.196 scale StandardScaler() 0 -> 0.5642751133773938
 2872/3780 0.350 12.196 scale MinMaxScaler()   0 -> 0.5582392623000909
 2873/3780 0.350 12.196 auto StandardScaler() 0 -> 0.564275113377397
 2874/3780 0.350 12.196 auto MinMaxScaler()   0 -> 0.5472864582633938
 2875/3780 0.350 12.196 0.01 StandardScaler() 0 -> 0.54526334822105
 2876/3780 0.350 12.196 0.01 MinMaxScaler()   0 -> 0.5846326716953506
 2877/3780 0.350 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5369052087270755
 2878/3780 0.350 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5752590019985373
 2879/3780 0.350 12.196 0.1 StandardScaler() 0 -> 0.5458062721140791
 2880/3780 0.350 12.196 0.1 MinMaxScaler()   0 -> 0.5547397973289204
 2881/3780 0.350 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5805356807565419
 2882/3780 0.350 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5439161390708998
 2883/3780 0.350 12.196 1.0 StandardScaler() 0 -> 0.6362117044798118
 2884/3780 0.350 12.196 1.0 MinMaxScaler()   0 -> 0.5366776792257376
 2885/3780 0.350 15.476 scale StandardScaler() 0 -> 0.5695136949133399
 2886/3780 0.350 15.476 scale MinMaxScaler()   0 -> 0.562182932530849
 2887/3780 0.350 15.476 auto StandardScaler() 0 -> 0.5695136949133436
 2888/3780 0.350 15.476 auto MinMaxScaler()   0 -> 0.5462825598097294
 2889/3780 0.350 15.476 0.01 StandardScaler() 0 -> 0.5445845878468795
 2890/3780 0.350 15.476 0.01 MinMaxScaler()   0 -> 0.5843422647511981
 2891/3780 0.350 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5368367044942113
 2892/3780 0.350 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5733402960756148
 2893/3780 0.350 15.476 0.1 StandardScaler() 0 -> 0.5462299401508964
 2894/3780 0.350 15.476 0.1 MinMaxScaler()   0 -> 0.5532157092230442
 2895/3780 0.350 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5876663121933318
 2896/3780 0.350 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5432367284570154
 2897/3780 0.350 15.476 1.0 StandardScaler() 0 -> 0.6513748146332956
 2898/3780 0.350 15.476 1.0 MinMaxScaler()   0 -> 0.5370409361005403
 2899/3780 0.350 19.638 scale StandardScaler() 0 -> 0.5756900993288667
 2900/3780 0.350 19.638 scale MinMaxScaler()   0 -> 0.5673114750345606
 2901/3780 0.350 19.638 auto StandardScaler() 0 -> 0.5756900993288534
 2902/3780 0.350 19.638 auto MinMaxScaler()   0 -> 0.5459777673230876
 2903/3780 0.350 19.638 0.01 StandardScaler() 0 -> 0.5443292471167761
 2904/3780 0.350 19.638 0.01 MinMaxScaler()   0 -> 0.5837934917876583
 2905/3780 0.350 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5366357304711924
 2906/3780 0.350 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5715090182970025
 2907/3780 0.350 19.638 0.1 StandardScaler() 0 -> 0.5464485953308471
 2908/3780 0.350 19.638 0.1 MinMaxScaler()   0 -> 0.5517775346402879
 2909/3780 0.350 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5979426032166265
 2910/3780 0.350 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5429643468141991
 2911/3780 0.350 19.638 1.0 StandardScaler() 0 -> 0.6682398590330568
 2912/3780 0.350 19.638 1.0 MinMaxScaler()   0 -> 0.5369419226973359
 2913/3780 0.350 24.920 scale StandardScaler() 0 -> 0.5816995748353864
 2914/3780 0.350 24.920 scale MinMaxScaler()   0 -> 0.5722077409486728
 2915/3780 0.350 24.920 auto StandardScaler() 0 -> 0.5816995748353849
 2916/3780 0.350 24.920 auto MinMaxScaler()   0 -> 0.5455280363015249
 2917/3780 0.350 24.920 0.01 StandardScaler() 0 -> 0.5439271806730215
 2918/3780 0.350 24.920 0.01 MinMaxScaler()   0 -> 0.5833324581597084
 2919/3780 0.350 24.920 0.03162277660168379 StandardScaler() 0 -> 0.537064790246296
 2920/3780 0.350 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5689343620335056
 2921/3780 0.350 24.920 0.1 StandardScaler() 0 -> 0.5483105038513896
 2922/3780 0.350 24.920 0.1 MinMaxScaler()   0 -> 0.550345932331465
 2923/3780 0.350 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6096497767445298
 2924/3780 0.350 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5425585255909349
 2925/3780 0.350 24.920 1.0 StandardScaler() 0 -> 0.6871727523002562
 2926/3780 0.350 24.920 1.0 MinMaxScaler()   0 -> 0.5371876790907657
 2927/3780 0.350 31.623 scale StandardScaler() 0 -> 0.587211856282826
 2928/3780 0.350 31.623 scale MinMaxScaler()   0 -> 0.5776392904402781
 2929/3780 0.350 31.623 auto StandardScaler() 0 -> 0.5872118562828245
 2930/3780 0.350 31.623 auto MinMaxScaler()   0 -> 0.5448662916546744
 2931/3780 0.350 31.623 0.01 StandardScaler() 0 -> 0.5434913281656638
 2932/3780 0.350 31.623 0.01 MinMaxScaler()   0 -> 0.582621135464705
 2933/3780 0.350 31.623 0.03162277660168379 StandardScaler() 0 -> 0.53746855107908
 2934/3780 0.350 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5667695211655465
 2935/3780 0.350 31.623 0.1 StandardScaler() 0 -> 0.5510345944212155
 2936/3780 0.350 31.623 0.1 MinMaxScaler()   0 -> 0.5491682918637331
 2937/3780 0.350 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6212606932989656
 2938/3780 0.350 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5420213779149224
 2939/3780 0.350 31.623 1.0 StandardScaler() 0 -> 0.7119606967389162
 2940/3780 0.350 31.623 1.0 MinMaxScaler()   0 -> 0.5376770687770841
 2941/3780 0.400 0.032 scale StandardScaler() 0 -> 0.5666379343874678
 2942/3780 0.400 0.032 scale MinMaxScaler()   0 -> 0.5641471994840132
 2943/3780 0.400 0.032 auto StandardScaler() 0 -> 0.5666379343874678
 2944/3780 0.400 0.032 auto MinMaxScaler()   0 -> 0.6088522713885284
 2945/3780 0.400 0.032 0.01 StandardScaler() 0 -> 0.6056095880098441
 2946/3780 0.400 0.032 0.01 MinMaxScaler()   0 -> 0.8321739487216999
 2947/3780 0.400 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5797422430829902
 2948/3780 0.400 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7888547393512289
 2949/3780 0.400 0.032 0.1 StandardScaler() 0 -> 0.5652357363286896
 2950/3780 0.400 0.032 0.1 MinMaxScaler()   0 -> 0.6830517030362966
 2951/3780 0.400 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5722504284061221
 2952/3780 0.400 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5941996692959958
 2953/3780 0.400 0.032 1.0 StandardScaler() 0 -> 0.6379605463886745
 2954/3780 0.400 0.032 1.0 MinMaxScaler()   0 -> 0.5755499112777266
 2955/3780 0.400 0.040 scale StandardScaler() 0 -> 0.5632061345035183
 2956/3780 0.400 0.040 scale MinMaxScaler()   0 -> 0.5614247011413769
 2957/3780 0.400 0.040 auto StandardScaler() 0 -> 0.5632061345035182
 2958/3780 0.400 0.040 auto MinMaxScaler()   0 -> 0.5986063745643061
 2959/3780 0.400 0.040 0.01 StandardScaler() 0 -> 0.5962788934837914
 2960/3780 0.400 0.040 0.01 MinMaxScaler()   0 -> 0.826475087794579
 2961/3780 0.400 0.040 0.03162277660168379 StandardScaler() 0 -> 0.576109276388221
 2962/3780 0.400 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7729356104398285
 2963/3780 0.400 0.040 0.1 StandardScaler() 0 -> 0.5619716064163617
 2964/3780 0.400 0.040 0.1 MinMaxScaler()   0 -> 0.6526033967568651
 2965/3780 0.400 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5673695595486444
 2966/3780 0.400 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5903889309402524
 2967/3780 0.400 0.040 1.0 StandardScaler() 0 -> 0.617669351561616
 2968/3780 0.400 0.040 1.0 MinMaxScaler()   0 -> 0.5727063833468026
 2969/3780 0.400 0.051 scale StandardScaler() 0 -> 0.5598771822823121
 2970/3780 0.400 0.051 scale MinMaxScaler()   0 -> 0.5592503232301943
 2971/3780 0.400 0.051 auto StandardScaler() 0 -> 0.5598771822823121
 2972/3780 0.400 0.051 auto MinMaxScaler()   0 -> 0.5940268546194676
 2973/3780 0.400 0.051 0.01 StandardScaler() 0 -> 0.591614264911405
 2974/3780 0.400 0.051 0.01 MinMaxScaler()   0 -> 0.8193098481555084
 2975/3780 0.400 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5726255783376409
 2976/3780 0.400 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7535998117547331
 2977/3780 0.400 0.051 0.1 StandardScaler() 0 -> 0.5593677274816563
 2978/3780 0.400 0.051 0.1 MinMaxScaler()   0 -> 0.624368194153149
 2979/3780 0.400 0.051 0.31622776601683794 StandardScaler() 0 -> 0.56295242653336
 2980/3780 0.400 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5861232748689164
 2981/3780 0.400 0.051 1.0 StandardScaler() 0 -> 0.6021287757301789
 2982/3780 0.400 0.051 1.0 MinMaxScaler()   0 -> 0.5699583193362029
 2983/3780 0.400 0.065 scale StandardScaler() 0 -> 0.5568761287252623
 2984/3780 0.400 0.065 scale MinMaxScaler()   0 -> 0.5569817580418758
 2985/3780 0.400 0.065 auto StandardScaler() 0 -> 0.5568761287252623
 2986/3780 0.400 0.065 auto MinMaxScaler()   0 -> 0.5908144137388908
 2987/3780 0.400 0.065 0.01 StandardScaler() 0 -> 0.5874919380748849
 2988/3780 0.400 0.065 0.01 MinMaxScaler()   0 -> 0.8104604732735082
 2989/3780 0.400 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5700935841192059
 2990/3780 0.400 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.7307346275569583
 2991/3780 0.400 0.065 0.1 StandardScaler() 0 -> 0.5569647677830201
 2992/3780 0.400 0.065 0.1 MinMaxScaler()   0 -> 0.6058994212005295
 2993/3780 0.400 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5589382699826236
 2994/3780 0.400 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5835897090500939
 2995/3780 0.400 0.065 1.0 StandardScaler() 0 -> 0.5894964244916183
 2996/3780 0.400 0.065 1.0 MinMaxScaler()   0 -> 0.5673411723622123
 2997/3780 0.400 0.082 scale StandardScaler() 0 -> 0.5541178646998
 2998/3780 0.400 0.082 scale MinMaxScaler()   0 -> 0.5547113389181658
 2999/3780 0.400 0.082 auto StandardScaler() 0 -> 0.5541178646998
 3000/3780 0.400 0.082 auto MinMaxScaler()   0 -> 0.5867773178709491
 3001/3780 0.400 0.082 0.01 StandardScaler() 0 -> 0.5839525157402968
 3002/3780 0.400 0.082 0.01 MinMaxScaler()   0 -> 0.7994485404489043
 3003/3780 0.400 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5671604029315641
 3004/3780 0.400 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.7042788475305111
 3005/3780 0.400 0.082 0.1 StandardScaler() 0 -> 0.5546002795927665
 3006/3780 0.400 0.082 0.1 MinMaxScaler()   0 -> 0.5980687941947755
 3007/3780 0.400 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5552892132256467
 3008/3780 0.400 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5816985217961069
 3009/3780 0.400 0.082 1.0 StandardScaler() 0 -> 0.5797546511292601
 3010/3780 0.400 0.082 1.0 MinMaxScaler()   0 -> 0.5645466014795959
 3011/3780 0.400 0.104 scale StandardScaler() 0 -> 0.5518864986840022
 3012/3780 0.400 0.104 scale MinMaxScaler()   0 -> 0.5527184766098107
 3013/3780 0.400 0.104 auto StandardScaler() 0 -> 0.5518864986840022
 3014/3780 0.400 0.104 auto MinMaxScaler()   0 -> 0.584691424326809
 3015/3780 0.400 0.104 0.01 StandardScaler() 0 -> 0.5819786675636319
 3016/3780 0.400 0.104 0.01 MinMaxScaler()   0 -> 0.785877807213495
 3017/3780 0.400 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5645397785546727
 3018/3780 0.400 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6748987012694219
 3019/3780 0.400 0.104 0.1 StandardScaler() 0 -> 0.5530652256561064
 3020/3780 0.400 0.104 0.1 MinMaxScaler()   0 -> 0.5937357101975255
 3021/3780 0.400 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5519566286929375
 3022/3780 0.400 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5795357776214259
 3023/3780 0.400 0.104 1.0 StandardScaler() 0 -> 0.5710703273665102
 3024/3780 0.400 0.104 1.0 MinMaxScaler()   0 -> 0.5622191520163539
 3025/3780 0.400 0.132 scale StandardScaler() 0 -> 0.5495307866704792
 3026/3780 0.400 0.132 scale MinMaxScaler()   0 -> 0.5513139278617966
 3027/3780 0.400 0.132 auto StandardScaler() 0 -> 0.5495307866704789
 3028/3780 0.400 0.132 auto MinMaxScaler()   0 -> 0.5830852103366562
 3029/3780 0.400 0.132 0.01 StandardScaler() 0 -> 0.5797091529169355
 3030/3780 0.400 0.132 0.01 MinMaxScaler()   0 -> 0.7693204446962518
 3031/3780 0.400 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5619300926268131
 3032/3780 0.400 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6449640545484064
 3033/3780 0.400 0.132 0.1 StandardScaler() 0 -> 0.5511001091691914
 3034/3780 0.400 0.132 0.1 MinMaxScaler()   0 -> 0.5907451043913715
 3035/3780 0.400 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5483908537343777
 3036/3780 0.400 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5773513286087834
 3037/3780 0.400 0.132 1.0 StandardScaler() 0 -> 0.564260303575796
 3038/3780 0.400 0.132 1.0 MinMaxScaler()   0 -> 0.5602633249710034
 3039/3780 0.400 0.168 scale StandardScaler() 0 -> 0.5477156283920402
 3040/3780 0.400 0.168 scale MinMaxScaler()   0 -> 0.5497740505970256
 3041/3780 0.400 0.168 auto StandardScaler() 0 -> 0.5477156283920402
 3042/3780 0.400 0.168 auto MinMaxScaler()   0 -> 0.5813614994638093
 3043/3780 0.400 0.168 0.01 StandardScaler() 0 -> 0.5779420970914527
 3044/3780 0.400 0.168 0.01 MinMaxScaler()   0 -> 0.7492708733275473
 3045/3780 0.400 0.168 0.03162277660168379 StandardScaler() 0 -> 0.559514574764306
 3046/3780 0.400 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6179860383369818
 3047/3780 0.400 0.168 0.1 StandardScaler() 0 -> 0.5492179978220801
 3048/3780 0.400 0.168 0.1 MinMaxScaler()   0 -> 0.5876478451049671
 3049/3780 0.400 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5459302544598358
 3050/3780 0.400 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5751281723477928
 3051/3780 0.400 0.168 1.0 StandardScaler() 0 -> 0.5581458270621926
 3052/3780 0.400 0.168 1.0 MinMaxScaler()   0 -> 0.5582164242804531
 3053/3780 0.400 0.213 scale StandardScaler() 0 -> 0.5460500510420442
 3054/3780 0.400 0.213 scale MinMaxScaler()   0 -> 0.5477958391577401
 3055/3780 0.400 0.213 auto StandardScaler() 0 -> 0.5460500510420441
 3056/3780 0.400 0.213 auto MinMaxScaler()   0 -> 0.579490873107531
 3057/3780 0.400 0.213 0.01 StandardScaler() 0 -> 0.5760349028169184
 3058/3780 0.400 0.213 0.01 MinMaxScaler()   0 -> 0.7256632034371892
 3059/3780 0.400 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5568973150996713
 3060/3780 0.400 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6039655942355928
 3061/3780 0.400 0.213 0.1 StandardScaler() 0 -> 0.5485684842370545
 3062/3780 0.400 0.213 0.1 MinMaxScaler()   0 -> 0.5860779630222361
 3063/3780 0.400 0.213 0.31622776601683794 StandardScaler() 0 -> 0.54409857750945
 3064/3780 0.400 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5731610310771533
 3065/3780 0.400 0.213 1.0 StandardScaler() 0 -> 0.5525404616264796
 3066/3780 0.400 0.213 1.0 MinMaxScaler()   0 -> 0.5559380070003734
 3067/3780 0.400 0.270 scale StandardScaler() 0 -> 0.5447200812243217
 3068/3780 0.400 0.270 scale MinMaxScaler()   0 -> 0.5458383100645897
 3069/3780 0.400 0.270 auto StandardScaler() 0 -> 0.5447200812243215
 3070/3780 0.400 0.270 auto MinMaxScaler()   0 -> 0.5778330966962556
 3071/3780 0.400 0.270 0.01 StandardScaler() 0 -> 0.5738994717267011
 3072/3780 0.400 0.270 0.01 MinMaxScaler()   0 -> 0.698474273999606
 3073/3780 0.400 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5547435925043405
 3074/3780 0.400 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5973468388322759
 3075/3780 0.400 0.270 0.1 StandardScaler() 0 -> 0.5474305476174379
 3076/3780 0.400 0.270 0.1 MinMaxScaler()   0 -> 0.5846067553868802
 3077/3780 0.400 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5426950118248464
 3078/3780 0.400 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5711038487991439
 3079/3780 0.400 0.270 1.0 StandardScaler() 0 -> 0.5487290995942495
 3080/3780 0.400 0.270 1.0 MinMaxScaler()   0 -> 0.5541646891256294
 3081/3780 0.400 0.342 scale StandardScaler() 0 -> 0.5434773495318641
 3082/3780 0.400 0.342 scale MinMaxScaler()   0 -> 0.5445406043091746
 3083/3780 0.400 0.342 auto StandardScaler() 0 -> 0.543477349531864
 3084/3780 0.400 0.342 auto MinMaxScaler()   0 -> 0.5759254218602515
 3085/3780 0.400 0.342 0.01 StandardScaler() 0 -> 0.5718661532641524
 3086/3780 0.400 0.342 0.01 MinMaxScaler()   0 -> 0.6688193956972032
 3087/3780 0.400 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5529955413634303
 3088/3780 0.400 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5938948195241026
 3089/3780 0.400 0.342 0.1 StandardScaler() 0 -> 0.5455681188103018
 3090/3780 0.400 0.342 0.1 MinMaxScaler()   0 -> 0.5835062400423395
 3091/3780 0.400 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5418014525409306
 3092/3780 0.400 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5691899232428251
 3093/3780 0.400 0.342 1.0 StandardScaler() 0 -> 0.5462269933376593
 3094/3780 0.400 0.342 1.0 MinMaxScaler()   0 -> 0.5525000704802147
 3095/3780 0.400 0.434 scale StandardScaler() 0 -> 0.542107964503958
 3096/3780 0.400 0.434 scale MinMaxScaler()   0 -> 0.5430876173091921
 3097/3780 0.400 0.434 auto StandardScaler() 0 -> 0.5421079645039582
 3098/3780 0.400 0.434 auto MinMaxScaler()   0 -> 0.5741721786138657
 3099/3780 0.400 0.434 0.01 StandardScaler() 0 -> 0.5696072967505871
 3100/3780 0.400 0.434 0.01 MinMaxScaler()   0 -> 0.6397876974878217
 3101/3780 0.400 0.434 0.03162277660168379 StandardScaler() 0 -> 0.55120441246035
 3102/3780 0.400 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.590591349232098
 3103/3780 0.400 0.434 0.1 StandardScaler() 0 -> 0.5435576152339897
 3104/3780 0.400 0.434 0.1 MinMaxScaler()   0 -> 0.5823621866218808
 3105/3780 0.400 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5404652032198901
 3106/3780 0.400 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5669517020988887
 3107/3780 0.400 0.434 1.0 StandardScaler() 0 -> 0.5445786530986901
 3108/3780 0.400 0.434 1.0 MinMaxScaler()   0 -> 0.5507868339316379
 3109/3780 0.400 0.551 scale StandardScaler() 0 -> 0.540752415516117
 3110/3780 0.400 0.551 scale MinMaxScaler()   0 -> 0.5419978252192856
 3111/3780 0.400 0.551 auto StandardScaler() 0 -> 0.5407524155161167
 3112/3780 0.400 0.551 auto MinMaxScaler()   0 -> 0.5721245938860812
 3113/3780 0.400 0.551 0.01 StandardScaler() 0 -> 0.5672053842368553
 3114/3780 0.400 0.551 0.01 MinMaxScaler()   0 -> 0.6141901402539963
 3115/3780 0.400 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5498577512927584
 3116/3780 0.400 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5883107243706411
 3117/3780 0.400 0.551 0.1 StandardScaler() 0 -> 0.5420662840650303
 3118/3780 0.400 0.551 0.1 MinMaxScaler()   0 -> 0.5810998022869212
 3119/3780 0.400 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5387891885897208
 3120/3780 0.400 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5646846994131405
 3121/3780 0.400 0.551 1.0 StandardScaler() 0 -> 0.5429781379327999
 3122/3780 0.400 0.551 1.0 MinMaxScaler()   0 -> 0.5499724177698255
 3123/3780 0.400 0.700 scale StandardScaler() 0 -> 0.5392622590549778
 3124/3780 0.400 0.700 scale MinMaxScaler()   0 -> 0.5405795463601002
 3125/3780 0.400 0.700 auto StandardScaler() 0 -> 0.5392622590549774
 3126/3780 0.400 0.700 auto MinMaxScaler()   0 -> 0.570316578217046
 3127/3780 0.400 0.700 0.01 StandardScaler() 0 -> 0.5650646402492062
 3128/3780 0.400 0.700 0.01 MinMaxScaler()   0 -> 0.6025385136922746
 3129/3780 0.400 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5492699185534633
 3130/3780 0.400 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.587051472198885
 3131/3780 0.400 0.700 0.1 StandardScaler() 0 -> 0.5406903475045287
 3132/3780 0.400 0.700 0.1 MinMaxScaler()   0 -> 0.5796699200262485
 3133/3780 0.400 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5378512405074768
 3134/3780 0.400 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5628389208793643
 3135/3780 0.400 0.700 1.0 StandardScaler() 0 -> 0.5427920453606995
 3136/3780 0.400 0.700 1.0 MinMaxScaler()   0 -> 0.5491045986066051
 3137/3780 0.400 0.888 scale StandardScaler() 0 -> 0.5386535356440806
 3138/3780 0.400 0.888 scale MinMaxScaler()   0 -> 0.5399030310103062
 3139/3780 0.400 0.888 auto StandardScaler() 0 -> 0.5386535356440803
 3140/3780 0.400 0.888 auto MinMaxScaler()   0 -> 0.5679625949183612
 3141/3780 0.400 0.888 0.01 StandardScaler() 0 -> 0.5624287576862087
 3142/3780 0.400 0.888 0.01 MinMaxScaler()   0 -> 0.596531167946042
 3143/3780 0.400 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5484500365304977
 3144/3780 0.400 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5861481417932074
 3145/3780 0.400 0.888 0.1 StandardScaler() 0 -> 0.5405333736329351
 3146/3780 0.400 0.888 0.1 MinMaxScaler()   0 -> 0.5782964569236045
 3147/3780 0.400 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5379908727575451
 3148/3780 0.400 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5608616708026876
 3149/3780 0.400 0.888 1.0 StandardScaler() 0 -> 0.5429125289260294
 3150/3780 0.400 0.888 1.0 MinMaxScaler()   0 -> 0.5481723828056017
 3151/3780 0.400 1.126 scale StandardScaler() 0 -> 0.5388332453878785
 3152/3780 0.400 1.126 scale MinMaxScaler()   0 -> 0.5400085816194142
 3153/3780 0.400 1.126 auto StandardScaler() 0 -> 0.538833245387878
 3154/3780 0.400 1.126 auto MinMaxScaler()   0 -> 0.5659878867177079
 3155/3780 0.400 1.126 0.01 StandardScaler() 0 -> 0.5602808837412528
 3156/3780 0.400 1.126 0.01 MinMaxScaler()   0 -> 0.5932185888151006
 3157/3780 0.400 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5474724249775771
 3158/3780 0.400 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5854911405791354
 3159/3780 0.400 1.126 0.1 StandardScaler() 0 -> 0.5405564851730145
 3160/3780 0.400 1.126 0.1 MinMaxScaler()   0 -> 0.5765467165871216
 3161/3780 0.400 1.126 0.31622776601683794 StandardScaler() 0 -> 0.5395023273910394
 3162/3780 0.400 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.558975523526465
 3163/3780 0.400 1.126 1.0 StandardScaler() 0 -> 0.5435073983441666
 3164/3780 0.400 1.126 1.0 MinMaxScaler()   0 -> 0.5469129043188571
 3165/3780 0.400 1.429 scale StandardScaler() 0 -> 0.5388522575781413
 3166/3780 0.400 1.429 scale MinMaxScaler()   0 -> 0.5397511113567601
 3167/3780 0.400 1.429 auto StandardScaler() 0 -> 0.5388522575781405
 3168/3780 0.400 1.429 auto MinMaxScaler()   0 -> 0.5638930535412197
 3169/3780 0.400 1.429 0.01 StandardScaler() 0 -> 0.5586266883187683
 3170/3780 0.400 1.429 0.01 MinMaxScaler()   0 -> 0.5903477312328903
 3171/3780 0.400 1.429 0.03162277660168379 StandardScaler() 0 -> 0.546714111006402
 3172/3780 0.400 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5848355081828206
 3173/3780 0.400 1.429 0.1 StandardScaler() 0 -> 0.5403435898218368
 3174/3780 0.400 1.429 0.1 MinMaxScaler()   0 -> 0.5751241326281971
 3175/3780 0.400 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5413962739774502
 3176/3780 0.400 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5573431565156596
 3177/3780 0.400 1.429 1.0 StandardScaler() 0 -> 0.5456494440173918
 3178/3780 0.400 1.429 1.0 MinMaxScaler()   0 -> 0.5459111999160551
 3179/3780 0.400 1.814 scale StandardScaler() 0 -> 0.539210105793248
 3180/3780 0.400 1.814 scale MinMaxScaler()   0 -> 0.5396775357552911
 3181/3780 0.400 1.814 auto StandardScaler() 0 -> 0.5392101057932472
 3182/3780 0.400 1.814 auto MinMaxScaler()   0 -> 0.562208933043079
 3183/3780 0.400 1.814 0.01 StandardScaler() 0 -> 0.5565425366009649
 3184/3780 0.400 1.814 0.01 MinMaxScaler()   0 -> 0.5884811338260377
 3185/3780 0.400 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5461019019581802
 3186/3780 0.400 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5842728218455292
 3187/3780 0.400 1.814 0.1 StandardScaler() 0 -> 0.5402606785624053
 3188/3780 0.400 1.814 0.1 MinMaxScaler()   0 -> 0.5733305467479739
 3189/3780 0.400 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5439005125256151
 3190/3780 0.400 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.5559998660850934
 3191/3780 0.400 1.814 1.0 StandardScaler() 0 -> 0.5485356331879501
 3192/3780 0.400 1.814 1.0 MinMaxScaler()   0 -> 0.5449698303575138
 3193/3780 0.400 2.302 scale StandardScaler() 0 -> 0.5402292361239053
 3194/3780 0.400 2.302 scale MinMaxScaler()   0 -> 0.5404732898154517
 3195/3780 0.400 2.302 auto StandardScaler() 0 -> 0.5402292361239049
 3196/3780 0.400 2.302 auto MinMaxScaler()   0 -> 0.5604095732602025
 3197/3780 0.400 2.302 0.01 StandardScaler() 0 -> 0.5548053132145542
 3198/3780 0.400 2.302 0.01 MinMaxScaler()   0 -> 0.5871874955693014
 3199/3780 0.400 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5451678046439773
 3200/3780 0.400 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5835380597089602
 3201/3780 0.400 2.302 0.1 StandardScaler() 0 -> 0.5401395380971671
 3202/3780 0.400 2.302 0.1 MinMaxScaler()   0 -> 0.5712344158693915
 3203/3780 0.400 2.302 0.31622776601683794 StandardScaler() 0 -> 0.54631488817517
 3204/3780 0.400 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5547248266379435
 3205/3780 0.400 2.302 1.0 StandardScaler() 0 -> 0.5529078182722094
 3206/3780 0.400 2.302 1.0 MinMaxScaler()   0 -> 0.5436916790951265
 3207/3780 0.400 2.921 scale StandardScaler() 0 -> 0.5415909018713108
 3208/3780 0.400 2.921 scale MinMaxScaler()   0 -> 0.540856152647014
 3209/3780 0.400 2.921 auto StandardScaler() 0 -> 0.54159090187131
 3210/3780 0.400 2.921 auto MinMaxScaler()   0 -> 0.5585296561565906
 3211/3780 0.400 2.921 0.01 StandardScaler() 0 -> 0.5534669061298937
 3212/3780 0.400 2.921 0.01 MinMaxScaler()   0 -> 0.5866454663669091
 3213/3780 0.400 2.921 0.03162277660168379 StandardScaler() 0 -> 0.5440933784946482
 3214/3780 0.400 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5828562420753902
 3215/3780 0.400 2.921 0.1 StandardScaler() 0 -> 0.5408371814491922
 3216/3780 0.400 2.921 0.1 MinMaxScaler()   0 -> 0.5692476407002068
 3217/3780 0.400 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5496504003857156
 3218/3780 0.400 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5536376680900043
 3219/3780 0.400 2.921 1.0 StandardScaler() 0 -> 0.5590898430394634
 3220/3780 0.400 2.921 1.0 MinMaxScaler()   0 -> 0.5425272842250345
 3221/3780 0.400 3.707 scale StandardScaler() 0 -> 0.543707637525476
 3222/3780 0.400 3.707 scale MinMaxScaler()   0 -> 0.5425071693685308
 3223/3780 0.400 3.707 auto StandardScaler() 0 -> 0.5437076375254768
 3224/3780 0.400 3.707 auto MinMaxScaler()   0 -> 0.5570927616313949
 3225/3780 0.400 3.707 0.01 StandardScaler() 0 -> 0.5529980233019608
 3226/3780 0.400 3.707 0.01 MinMaxScaler()   0 -> 0.5862627293294148
 3227/3780 0.400 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5431444633067121
 3228/3780 0.400 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5820824435423954
 3229/3780 0.400 3.707 0.1 StandardScaler() 0 -> 0.5415264077915087
 3230/3780 0.400 3.707 0.1 MinMaxScaler()   0 -> 0.5672438180976802
 3231/3780 0.400 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5536557834257522
 3232/3780 0.400 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5532513617627318
 3233/3780 0.400 3.707 1.0 StandardScaler() 0 -> 0.5670642829157203
 3234/3780 0.400 3.707 1.0 MinMaxScaler()   0 -> 0.5415466276460307
 3235/3780 0.400 4.703 scale StandardScaler() 0 -> 0.5461367473509177
 3236/3780 0.400 4.703 scale MinMaxScaler()   0 -> 0.5444749999223731
 3237/3780 0.400 4.703 auto StandardScaler() 0 -> 0.546136747350915
 3238/3780 0.400 4.703 auto MinMaxScaler()   0 -> 0.5554172362031701
 3239/3780 0.400 4.703 0.01 StandardScaler() 0 -> 0.5521311101403034
 3240/3780 0.400 4.703 0.01 MinMaxScaler()   0 -> 0.5857778922967506
 3241/3780 0.400 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5422376610784593
 3242/3780 0.400 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5811146274103725
 3243/3780 0.400 4.703 0.1 StandardScaler() 0 -> 0.5420909957402211
 3244/3780 0.400 4.703 0.1 MinMaxScaler()   0 -> 0.5652359406138313
 3245/3780 0.400 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5574886791748308
 3246/3780 0.400 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5528623026659901
 3247/3780 0.400 4.703 1.0 StandardScaler() 0 -> 0.5777794009469766
 3248/3780 0.400 4.703 1.0 MinMaxScaler()   0 -> 0.5411898555829519
 3249/3780 0.400 5.968 scale StandardScaler() 0 -> 0.5496822007037538
 3250/3780 0.400 5.968 scale MinMaxScaler()   0 -> 0.5467570828647909
 3251/3780 0.400 5.968 auto StandardScaler() 0 -> 0.5496822007037535
 3252/3780 0.400 5.968 auto MinMaxScaler()   0 -> 0.554628229067624
 3253/3780 0.400 5.968 0.01 StandardScaler() 0 -> 0.5516751775313998
 3254/3780 0.400 5.968 0.01 MinMaxScaler()   0 -> 0.5854569753534943
 3255/3780 0.400 5.968 0.03162277660168379 StandardScaler() 0 -> 0.541477335317595
 3256/3780 0.400 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5801068381417567
 3257/3780 0.400 5.968 0.1 StandardScaler() 0 -> 0.542249479375124
 3258/3780 0.400 5.968 0.1 MinMaxScaler()   0 -> 0.5635879233637565
 3259/3780 0.400 5.968 0.31622776601683794 StandardScaler() 0 -> 0.561556972017719
 3260/3780 0.400 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5523388498618681
 3261/3780 0.400 5.968 1.0 StandardScaler() 0 -> 0.5892344775881782
 3262/3780 0.400 5.968 1.0 MinMaxScaler()   0 -> 0.5410961308824067
 3263/3780 0.400 7.574 scale StandardScaler() 0 -> 0.5555899037826587
 3264/3780 0.400 7.574 scale MinMaxScaler()   0 -> 0.5506745912856043
 3265/3780 0.400 7.574 auto StandardScaler() 0 -> 0.5555899037826576
 3266/3780 0.400 7.574 auto MinMaxScaler()   0 -> 0.5537095102348161
 3267/3780 0.400 7.574 0.01 StandardScaler() 0 -> 0.5514727261577983
 3268/3780 0.400 7.574 0.01 MinMaxScaler()   0 -> 0.5850762791035135
 3269/3780 0.400 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5411273013753927
 3270/3780 0.400 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5789777959910778
 3271/3780 0.400 7.574 0.1 StandardScaler() 0 -> 0.5435283499982299
 3272/3780 0.400 7.574 0.1 MinMaxScaler()   0 -> 0.5617322733953904
 3273/3780 0.400 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5656319828978943
 3274/3780 0.400 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5515314925598708
 3275/3780 0.400 7.574 1.0 StandardScaler() 0 -> 0.6012604790685941
 3276/3780 0.400 7.574 1.0 MinMaxScaler()   0 -> 0.5409528103122447
 3277/3780 0.400 9.611 scale StandardScaler() 0 -> 0.5620255850810753
 3278/3780 0.400 9.611 scale MinMaxScaler()   0 -> 0.556718426209959
 3279/3780 0.400 9.611 auto StandardScaler() 0 -> 0.5620255850810695
 3280/3780 0.400 9.611 auto MinMaxScaler()   0 -> 0.5536096480775377
 3281/3780 0.400 9.611 0.01 StandardScaler() 0 -> 0.5508744548596333
 3282/3780 0.400 9.611 0.01 MinMaxScaler()   0 -> 0.584668618453704
 3283/3780 0.400 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5405480729819789
 3284/3780 0.400 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5775472395989812
 3285/3780 0.400 9.611 0.1 StandardScaler() 0 -> 0.5452497468184463
 3286/3780 0.400 9.611 0.1 MinMaxScaler()   0 -> 0.5599429030325823
 3287/3780 0.400 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5709086626607002
 3288/3780 0.400 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.5504806011226303
 3289/3780 0.400 9.611 1.0 StandardScaler() 0 -> 0.6138200782222983
 3290/3780 0.400 9.611 1.0 MinMaxScaler()   0 -> 0.5403671053352467
 3291/3780 0.400 12.196 scale StandardScaler() 0 -> 0.5685085438097613
 3292/3780 0.400 12.196 scale MinMaxScaler()   0 -> 0.56084797616615
 3293/3780 0.400 12.196 auto StandardScaler() 0 -> 0.5685085438097617
 3294/3780 0.400 12.196 auto MinMaxScaler()   0 -> 0.5533290972420365
 3295/3780 0.400 12.196 0.01 StandardScaler() 0 -> 0.5503653573217435
 3296/3780 0.400 12.196 0.01 MinMaxScaler()   0 -> 0.5843446897585002
 3297/3780 0.400 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5393780949131926
 3298/3780 0.400 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5758205307535614
 3299/3780 0.400 12.196 0.1 StandardScaler() 0 -> 0.5465236605466018
 3300/3780 0.400 12.196 0.1 MinMaxScaler()   0 -> 0.5580560843257891
 3301/3780 0.400 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5769792299227522
 3302/3780 0.400 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5496529246753269
 3303/3780 0.400 12.196 1.0 StandardScaler() 0 -> 0.6265100279158307
 3304/3780 0.400 12.196 1.0 MinMaxScaler()   0 -> 0.5407816152398146
 3305/3780 0.400 15.476 scale StandardScaler() 0 -> 0.5730378053674847
 3306/3780 0.400 15.476 scale MinMaxScaler()   0 -> 0.5652855515220562
 3307/3780 0.400 15.476 auto StandardScaler() 0 -> 0.5730378053674873
 3308/3780 0.400 15.476 auto MinMaxScaler()   0 -> 0.5533489869356777
 3309/3780 0.400 15.476 0.01 StandardScaler() 0 -> 0.5502124331521183
 3310/3780 0.400 15.476 0.01 MinMaxScaler()   0 -> 0.5840196341553944
 3311/3780 0.400 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5394528469119425
 3312/3780 0.400 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5742540862518679
 3313/3780 0.400 15.476 0.1 StandardScaler() 0 -> 0.547005220113372
 3314/3780 0.400 15.476 0.1 MinMaxScaler()   0 -> 0.5569862170383605
 3315/3780 0.400 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5832979366384419
 3316/3780 0.400 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5491771832054432
 3317/3780 0.400 15.476 1.0 StandardScaler() 0 -> 0.640544989282183
 3318/3780 0.400 15.476 1.0 MinMaxScaler()   0 -> 0.5409692416586643
 3319/3780 0.400 19.638 scale StandardScaler() 0 -> 0.5765247892740496
 3320/3780 0.400 19.638 scale MinMaxScaler()   0 -> 0.5693309043428241
 3321/3780 0.400 19.638 auto StandardScaler() 0 -> 0.5765247892740439
 3322/3780 0.400 19.638 auto MinMaxScaler()   0 -> 0.5528711982014606
 3323/3780 0.400 19.638 0.01 StandardScaler() 0 -> 0.5502481823485825
 3324/3780 0.400 19.638 0.01 MinMaxScaler()   0 -> 0.5835732047820493
 3325/3780 0.400 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5395993215617192
 3326/3780 0.400 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.572197827950521
 3327/3780 0.400 19.638 0.1 StandardScaler() 0 -> 0.5485559716638343
 3328/3780 0.400 19.638 0.1 MinMaxScaler()   0 -> 0.5555337590225138
 3329/3780 0.400 19.638 0.31622776601683794 StandardScaler() 0 -> 0.592481088781511
 3330/3780 0.400 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5480744959465341
 3331/3780 0.400 19.638 1.0 StandardScaler() 0 -> 0.6561817427096489
 3332/3780 0.400 19.638 1.0 MinMaxScaler()   0 -> 0.5410607692918993
 3333/3780 0.400 24.920 scale StandardScaler() 0 -> 0.5806426954337772
 3334/3780 0.400 24.920 scale MinMaxScaler()   0 -> 0.5730635375572882
 3335/3780 0.400 24.920 auto StandardScaler() 0 -> 0.580642695433785
 3336/3780 0.400 24.920 auto MinMaxScaler()   0 -> 0.5521124563766915
 3337/3780 0.400 24.920 0.01 StandardScaler() 0 -> 0.5497910765010309
 3338/3780 0.400 24.920 0.01 MinMaxScaler()   0 -> 0.5829002077296229
 3339/3780 0.400 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5394505531193658
 3340/3780 0.400 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5702276575410271
 3341/3780 0.400 24.920 0.1 StandardScaler() 0 -> 0.5506781770550324
 3342/3780 0.400 24.920 0.1 MinMaxScaler()   0 -> 0.5546283863146776
 3343/3780 0.400 24.920 0.31622776601683794 StandardScaler() 0 -> 0.6026303990971171
 3344/3780 0.400 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5473505505367064
 3345/3780 0.400 24.920 1.0 StandardScaler() 0 -> 0.6764835146967317
 3346/3780 0.400 24.920 1.0 MinMaxScaler()   0 -> 0.5409728560505308
 3347/3780 0.400 31.623 scale StandardScaler() 0 -> 0.5853046402512914
 3348/3780 0.400 31.623 scale MinMaxScaler()   0 -> 0.5781081138263654
 3349/3780 0.400 31.623 auto StandardScaler() 0 -> 0.5853046402512949
 3350/3780 0.400 31.623 auto MinMaxScaler()   0 -> 0.5511893158106195
 3351/3780 0.400 31.623 0.01 StandardScaler() 0 -> 0.5494915513191218
 3352/3780 0.400 31.623 0.01 MinMaxScaler()   0 -> 0.582290108228571
 3353/3780 0.400 31.623 0.03162277660168379 StandardScaler() 0 -> 0.5397826584326526
 3354/3780 0.400 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5685087249372138
 3355/3780 0.400 31.623 0.1 StandardScaler() 0 -> 0.5538216601123248
 3356/3780 0.400 31.623 0.1 MinMaxScaler()   0 -> 0.5543314526203642
 3357/3780 0.400 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6129336321560701
 3358/3780 0.400 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5463919614311026
 3359/3780 0.400 31.623 1.0 StandardScaler() 0 -> 0.7011252482213971
 3360/3780 0.400 31.623 1.0 MinMaxScaler()   0 -> 0.5411819330956611
 3361/3780 0.450 0.032 scale StandardScaler() 0 -> 0.5714651302600123
 3362/3780 0.450 0.032 scale MinMaxScaler()   0 -> 0.5707238226169635
 3363/3780 0.450 0.032 auto StandardScaler() 0 -> 0.5714651302600123
 3364/3780 0.450 0.032 auto MinMaxScaler()   0 -> 0.6132988108228727
 3365/3780 0.450 0.032 0.01 StandardScaler() 0 -> 0.6102899998608655
 3366/3780 0.450 0.032 0.01 MinMaxScaler()   0 -> 0.8646015255634668
 3367/3780 0.450 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5813929882083022
 3368/3780 0.450 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.8183070124908146
 3369/3780 0.450 0.032 0.1 StandardScaler() 0 -> 0.569153788303223
 3370/3780 0.450 0.032 0.1 MinMaxScaler()   0 -> 0.7034401359535994
 3371/3780 0.450 0.032 0.31622776601683794 StandardScaler() 0 -> 0.5774435986357157
 3372/3780 0.450 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5950943583314368
 3373/3780 0.450 0.032 1.0 StandardScaler() 0 -> 0.6472980604890991
 3374/3780 0.450 0.032 1.0 MinMaxScaler()   0 -> 0.578044947587919
 3375/3780 0.450 0.040 scale StandardScaler() 0 -> 0.5688233973193741
 3376/3780 0.450 0.040 scale MinMaxScaler()   0 -> 0.5678823189289571
 3377/3780 0.450 0.040 auto StandardScaler() 0 -> 0.568823397319374
 3378/3780 0.450 0.040 auto MinMaxScaler()   0 -> 0.5997016016032353
 3379/3780 0.450 0.040 0.01 StandardScaler() 0 -> 0.597036583795885
 3380/3780 0.450 0.040 0.01 MinMaxScaler()   0 -> 0.8585464658527465
 3381/3780 0.450 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5784046606484834
 3382/3780 0.450 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.8012291243040801
 3383/3780 0.450 0.040 0.1 StandardScaler() 0 -> 0.5665946072778019
 3384/3780 0.450 0.040 0.1 MinMaxScaler()   0 -> 0.6683600889946447
 3385/3780 0.450 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5727151367255455
 3386/3780 0.450 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5909259123858118
 3387/3780 0.450 0.040 1.0 StandardScaler() 0 -> 0.6244044554204665
 3388/3780 0.450 0.040 1.0 MinMaxScaler()   0 -> 0.5757035389317169
 3389/3780 0.450 0.051 scale StandardScaler() 0 -> 0.5652107395388501
 3390/3780 0.450 0.051 scale MinMaxScaler()   0 -> 0.565856432190706
 3391/3780 0.450 0.051 auto StandardScaler() 0 -> 0.5652107395388501
 3392/3780 0.450 0.051 auto MinMaxScaler()   0 -> 0.5944043515883681
 3393/3780 0.450 0.051 0.01 StandardScaler() 0 -> 0.5918138214510305
 3394/3780 0.450 0.051 0.01 MinMaxScaler()   0 -> 0.8509558587978344
 3395/3780 0.450 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5754012838081851
 3396/3780 0.450 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.780697467514219
 3397/3780 0.450 0.051 0.1 StandardScaler() 0 -> 0.5645922201804288
 3398/3780 0.450 0.051 0.1 MinMaxScaler()   0 -> 0.6340254551064285
 3399/3780 0.450 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5681532677500901
 3400/3780 0.450 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5882468102343329
 3401/3780 0.450 0.051 1.0 StandardScaler() 0 -> 0.6065433179608785
 3402/3780 0.450 0.051 1.0 MinMaxScaler()   0 -> 0.5737711626215037
 3403/3780 0.450 0.065 scale StandardScaler() 0 -> 0.5627434854200778
 3404/3780 0.450 0.065 scale MinMaxScaler()   0 -> 0.5636661261708912
 3405/3780 0.450 0.065 auto StandardScaler() 0 -> 0.5627434854200778
 3406/3780 0.450 0.065 auto MinMaxScaler()   0 -> 0.5910955081659715
 3407/3780 0.450 0.065 0.01 StandardScaler() 0 -> 0.5888852380369242
 3408/3780 0.450 0.065 0.01 MinMaxScaler()   0 -> 0.8414825530345812
 3409/3780 0.450 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5727341182328934
 3410/3780 0.450 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.7564800227244483
 3411/3780 0.450 0.065 0.1 StandardScaler() 0 -> 0.5625103249663876
 3412/3780 0.450 0.065 0.1 MinMaxScaler()   0 -> 0.6092129600356513
 3413/3780 0.450 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5639136151297294
 3414/3780 0.450 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5859812425718834
 3415/3780 0.450 0.065 1.0 StandardScaler() 0 -> 0.5934084850130682
 3416/3780 0.450 0.065 1.0 MinMaxScaler()   0 -> 0.5710222178484193
 3417/3780 0.450 0.082 scale StandardScaler() 0 -> 0.5598716919367442
 3418/3780 0.450 0.082 scale MinMaxScaler()   0 -> 0.5614023259144963
 3419/3780 0.450 0.082 auto StandardScaler() 0 -> 0.5598716919367442
 3420/3780 0.450 0.082 auto MinMaxScaler()   0 -> 0.5889207196538839
 3421/3780 0.450 0.082 0.01 StandardScaler() 0 -> 0.5863934755689214
 3422/3780 0.450 0.082 0.01 MinMaxScaler()   0 -> 0.8296500192202018
 3423/3780 0.450 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5698071542156692
 3424/3780 0.450 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.7268660984737743
 3425/3780 0.450 0.082 0.1 StandardScaler() 0 -> 0.5606457136065841
 3426/3780 0.450 0.082 0.1 MinMaxScaler()   0 -> 0.599148507826449
 3427/3780 0.450 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5607727024266239
 3428/3780 0.450 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.583274264006869
 3429/3780 0.450 0.082 1.0 StandardScaler() 0 -> 0.5846640705056584
 3430/3780 0.450 0.082 1.0 MinMaxScaler()   0 -> 0.5687091188826786
 3431/3780 0.450 0.104 scale StandardScaler() 0 -> 0.5574893888894258
 3432/3780 0.450 0.104 scale MinMaxScaler()   0 -> 0.5597096517916156
 3433/3780 0.450 0.104 auto StandardScaler() 0 -> 0.5574893888894258
 3434/3780 0.450 0.104 auto MinMaxScaler()   0 -> 0.586707877945848
 3435/3780 0.450 0.104 0.01 StandardScaler() 0 -> 0.5839567875038584
 3436/3780 0.450 0.104 0.01 MinMaxScaler()   0 -> 0.8151158104164252
 3437/3780 0.450 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5675096928831432
 3438/3780 0.450 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6940711634266871
 3439/3780 0.450 0.104 0.1 StandardScaler() 0 -> 0.5594883490566002
 3440/3780 0.450 0.104 0.1 MinMaxScaler()   0 -> 0.5941221760892818
 3441/3780 0.450 0.104 0.31622776601683794 StandardScaler() 0 -> 0.556958779527489
 3442/3780 0.450 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5809804056420366
 3443/3780 0.450 0.104 1.0 StandardScaler() 0 -> 0.5757320425301086
 3444/3780 0.450 0.104 1.0 MinMaxScaler()   0 -> 0.5666134681185442
 3445/3780 0.450 0.132 scale StandardScaler() 0 -> 0.5552209557187907
 3446/3780 0.450 0.132 scale MinMaxScaler()   0 -> 0.5575798354333896
 3447/3780 0.450 0.132 auto StandardScaler() 0 -> 0.5552209557187906
 3448/3780 0.450 0.132 auto MinMaxScaler()   0 -> 0.5848624427805493
 3449/3780 0.450 0.132 0.01 StandardScaler() 0 -> 0.5815904643392912
 3450/3780 0.450 0.132 0.01 MinMaxScaler()   0 -> 0.797440277051218
 3451/3780 0.450 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5655652960179359
 3452/3780 0.450 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6587123398852625
 3453/3780 0.450 0.132 0.1 StandardScaler() 0 -> 0.558352404667581
 3454/3780 0.450 0.132 0.1 MinMaxScaler()   0 -> 0.5916162346270406
 3455/3780 0.450 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5538093987190459
 3456/3780 0.450 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5789747223865956
 3457/3780 0.450 0.132 1.0 StandardScaler() 0 -> 0.5690169440896765
 3458/3780 0.450 0.132 1.0 MinMaxScaler()   0 -> 0.5646548751050885
 3459/3780 0.450 0.168 scale StandardScaler() 0 -> 0.5527532196560596
 3460/3780 0.450 0.168 scale MinMaxScaler()   0 -> 0.5554968159825181
 3461/3780 0.450 0.168 auto StandardScaler() 0 -> 0.5527532196560595
 3462/3780 0.450 0.168 auto MinMaxScaler()   0 -> 0.5825305591533647
 3463/3780 0.450 0.168 0.01 StandardScaler() 0 -> 0.5796132772374628
 3464/3780 0.450 0.168 0.01 MinMaxScaler()   0 -> 0.7761492958276043
 3465/3780 0.450 0.168 0.03162277660168379 StandardScaler() 0 -> 0.563204394473879
 3466/3780 0.450 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6253662670541843
 3467/3780 0.450 0.168 0.1 StandardScaler() 0 -> 0.5562219565531676
 3468/3780 0.450 0.168 0.1 MinMaxScaler()   0 -> 0.5893654247131345
 3469/3780 0.450 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5521021821665548
 3470/3780 0.450 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5770244272925313
 3471/3780 0.450 0.168 1.0 StandardScaler() 0 -> 0.5629109155638679
 3472/3780 0.450 0.168 1.0 MinMaxScaler()   0 -> 0.562780091217341
 3473/3780 0.450 0.213 scale StandardScaler() 0 -> 0.5505072336231108
 3474/3780 0.450 0.213 scale MinMaxScaler()   0 -> 0.5531947852032503
 3475/3780 0.450 0.213 auto StandardScaler() 0 -> 0.5505072336231107
 3476/3780 0.450 0.213 auto MinMaxScaler()   0 -> 0.580683928380969
 3477/3780 0.450 0.213 0.01 StandardScaler() 0 -> 0.5773612580008622
 3478/3780 0.450 0.213 0.01 MinMaxScaler()   0 -> 0.7509435055791572
 3479/3780 0.450 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5609438690613845
 3480/3780 0.450 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6058578240134603
 3481/3780 0.450 0.213 0.1 StandardScaler() 0 -> 0.5549837576415491
 3482/3780 0.450 0.213 0.1 MinMaxScaler()   0 -> 0.5876971911521102
 3483/3780 0.450 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5504053983729533
 3484/3780 0.450 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5750420033066518
 3485/3780 0.450 0.213 1.0 StandardScaler() 0 -> 0.5576116537592807
 3486/3780 0.450 0.213 1.0 MinMaxScaler()   0 -> 0.5611756485204289
 3487/3780 0.450 0.270 scale StandardScaler() 0 -> 0.5488916645841647
 3488/3780 0.450 0.270 scale MinMaxScaler()   0 -> 0.550686972480178
 3489/3780 0.450 0.270 auto StandardScaler() 0 -> 0.5488916645841646
 3490/3780 0.450 0.270 auto MinMaxScaler()   0 -> 0.5791344709404443
 3491/3780 0.450 0.270 0.01 StandardScaler() 0 -> 0.5751995286232284
 3492/3780 0.450 0.270 0.01 MinMaxScaler()   0 -> 0.7207221011041652
 3493/3780 0.450 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5591107933727978
 3494/3780 0.450 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5981391673018494
 3495/3780 0.450 0.270 0.1 StandardScaler() 0 -> 0.5534488968182156
 3496/3780 0.450 0.270 0.1 MinMaxScaler()   0 -> 0.5861531792693108
 3497/3780 0.450 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5488158487831171
 3498/3780 0.450 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5730431924041633
 3499/3780 0.450 0.270 1.0 StandardScaler() 0 -> 0.5539957570549026
 3500/3780 0.450 0.270 1.0 MinMaxScaler()   0 -> 0.5602069417265149
 3501/3780 0.450 0.342 scale StandardScaler() 0 -> 0.5478412214220019
 3502/3780 0.450 0.342 scale MinMaxScaler()   0 -> 0.5492448154830473
 3503/3780 0.450 0.342 auto StandardScaler() 0 -> 0.5478412214220016
 3504/3780 0.450 0.342 auto MinMaxScaler()   0 -> 0.5775049181188942
 3505/3780 0.450 0.342 0.01 StandardScaler() 0 -> 0.5733092126864519
 3506/3780 0.450 0.342 0.01 MinMaxScaler()   0 -> 0.6865901278427607
 3507/3780 0.450 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5578199586686324
 3508/3780 0.450 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5938457094536109
 3509/3780 0.450 0.342 0.1 StandardScaler() 0 -> 0.5516470757008566
 3510/3780 0.450 0.342 0.1 MinMaxScaler()   0 -> 0.5845086474129809
 3511/3780 0.450 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5473377291874875
 3512/3780 0.450 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5707322946432817
 3513/3780 0.450 0.342 1.0 StandardScaler() 0 -> 0.550580289282598
 3514/3780 0.450 0.342 1.0 MinMaxScaler()   0 -> 0.559653864958069
 3515/3780 0.450 0.434 scale StandardScaler() 0 -> 0.54592288014466
 3516/3780 0.450 0.434 scale MinMaxScaler()   0 -> 0.5477656397129956
 3517/3780 0.450 0.434 auto StandardScaler() 0 -> 0.54592288014466
 3518/3780 0.450 0.434 auto MinMaxScaler()   0 -> 0.5754560699087866
 3519/3780 0.450 0.434 0.01 StandardScaler() 0 -> 0.5711769810163511
 3520/3780 0.450 0.434 0.01 MinMaxScaler()   0 -> 0.6519331138800042
 3521/3780 0.450 0.434 0.03162277660168379 StandardScaler() 0 -> 0.5571661483169218
 3522/3780 0.450 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5918243020600794
 3523/3780 0.450 0.434 0.1 StandardScaler() 0 -> 0.5499483762663422
 3524/3780 0.450 0.434 0.1 MinMaxScaler()   0 -> 0.5831292106466851
 3525/3780 0.450 0.434 0.31622776601683794 StandardScaler() 0 -> 0.5455001369389166
 3526/3780 0.450 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5689964866170437
 3527/3780 0.450 0.434 1.0 StandardScaler() 0 -> 0.5480615176432723
 3528/3780 0.450 0.434 1.0 MinMaxScaler()   0 -> 0.559000895629247
 3529/3780 0.450 0.551 scale StandardScaler() 0 -> 0.5447928218044089
 3530/3780 0.450 0.551 scale MinMaxScaler()   0 -> 0.5464595504209453
 3531/3780 0.450 0.551 auto StandardScaler() 0 -> 0.5447928218044086
 3532/3780 0.450 0.551 auto MinMaxScaler()   0 -> 0.5737007370314774
 3533/3780 0.450 0.551 0.01 StandardScaler() 0 -> 0.5690181002560598
 3534/3780 0.450 0.551 0.01 MinMaxScaler()   0 -> 0.620432081164441
 3535/3780 0.450 0.551 0.03162277660168379 StandardScaler() 0 -> 0.5564769501063461
 3536/3780 0.450 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5898802136740623
 3537/3780 0.450 0.551 0.1 StandardScaler() 0 -> 0.5492177197704051
 3538/3780 0.450 0.551 0.1 MinMaxScaler()   0 -> 0.5819046018009577
 3539/3780 0.450 0.551 0.31622776601683794 StandardScaler() 0 -> 0.5437887494055335
 3540/3780 0.450 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5674561545951278
 3541/3780 0.450 0.551 1.0 StandardScaler() 0 -> 0.5462201323704924
 3542/3780 0.450 0.551 1.0 MinMaxScaler()   0 -> 0.5584096961864654
 3543/3780 0.450 0.700 scale StandardScaler() 0 -> 0.5439815499836643
 3544/3780 0.450 0.700 scale MinMaxScaler()   0 -> 0.545250931703893
 3545/3780 0.450 0.700 auto StandardScaler() 0 -> 0.543981549983664
 3546/3780 0.450 0.700 auto MinMaxScaler()   0 -> 0.5716781596842627
 3547/3780 0.450 0.700 0.01 StandardScaler() 0 -> 0.5674760478187837
 3548/3780 0.450 0.700 0.01 MinMaxScaler()   0 -> 0.603029970652167
 3549/3780 0.450 0.700 0.03162277660168379 StandardScaler() 0 -> 0.5564769133699422
 3550/3780 0.450 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5883410187495048
 3551/3780 0.450 0.700 0.1 StandardScaler() 0 -> 0.5471426988356427
 3552/3780 0.450 0.700 0.1 MinMaxScaler()   0 -> 0.580612336076044
 3553/3780 0.450 0.700 0.31622776601683794 StandardScaler() 0 -> 0.5424540432813508
 3554/3780 0.450 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5660890897271244
 3555/3780 0.450 0.700 1.0 StandardScaler() 0 -> 0.5450007267480649
 3556/3780 0.450 0.700 1.0 MinMaxScaler()   0 -> 0.5571189476344434
 3557/3780 0.450 0.888 scale StandardScaler() 0 -> 0.5436952146819952
 3558/3780 0.450 0.888 scale MinMaxScaler()   0 -> 0.5446893185955358
 3559/3780 0.450 0.888 auto StandardScaler() 0 -> 0.5436952146819952
 3560/3780 0.450 0.888 auto MinMaxScaler()   0 -> 0.5699984810832739
 3561/3780 0.450 0.888 0.01 StandardScaler() 0 -> 0.5650202266507746
 3562/3780 0.450 0.888 0.01 MinMaxScaler()   0 -> 0.5976411952409394
 3563/3780 0.450 0.888 0.03162277660168379 StandardScaler() 0 -> 0.5553478659488049
 3564/3780 0.450 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5873556187455087
 3565/3780 0.450 0.888 0.1 StandardScaler() 0 -> 0.5459421078346716
 3566/3780 0.450 0.888 0.1 MinMaxScaler()   0 -> 0.5790701884927086
 3567/3780 0.450 0.888 0.31622776601683794 StandardScaler() 0 -> 0.5422840664464271
 3568/3780 0.450 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5643870581787928
 3569/3780 0.450 0.888 1.0 StandardScaler() 0 -> 0.5438703011654358
 3570/3780 0.450 0.888 1.0 MinMaxScaler()   0 -> 0.556243155761857
 3571/3780 0.450 1.126 scale StandardScaler() 0 -> 0.5429594448268732
 3572/3780 0.450 1.126 scale MinMaxScaler()   0 -> 0.5449092930985056
 3573/3780 0.450 1.126 auto StandardScaler() 0 -> 0.5429594448268725
 3574/3780 0.450 1.126 auto MinMaxScaler()   0 -> 0.5682110432001698
 3575/3780 0.450 1.126 0.01 StandardScaler() 0 -> 0.5629130903799116
 3576/3780 0.450 1.126 0.01 MinMaxScaler()   0 -> 0.5933360380400426
 3577/3780 0.450 1.126 0.03162277660168379 StandardScaler() 0 -> 0.5550914013057715
 3578/3780 0.450 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5859384941928689
 3579/3780 0.450 1.126 0.1 StandardScaler() 0 -> 0.5459879813500448
 3580/3780 0.450 1.126 0.1 MinMaxScaler()   0 -> 0.5773628038422701
 3581/3780 0.450 1.126 0.31622776601683794 StandardScaler() 0 -> 0.542636015484537
 3582/3780 0.450 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5629677617845
 3583/3780 0.450 1.126 1.0 StandardScaler() 0 -> 0.5439568480558511
 3584/3780 0.450 1.126 1.0 MinMaxScaler()   0 -> 0.5557008216575384
 3585/3780 0.450 1.429 scale StandardScaler() 0 -> 0.5423874007288841
 3586/3780 0.450 1.429 scale MinMaxScaler()   0 -> 0.5454044540522299
 3587/3780 0.450 1.429 auto StandardScaler() 0 -> 0.5423874007288841
 3588/3780 0.450 1.429 auto MinMaxScaler()   0 -> 0.5668001592082277
 3589/3780 0.450 1.429 0.01 StandardScaler() 0 -> 0.5609523010925617
 3590/3780 0.450 1.429 0.01 MinMaxScaler()   0 -> 0.5917104442980442
 3591/3780 0.450 1.429 0.03162277660168379 StandardScaler() 0 -> 0.5543154107794593
 3592/3780 0.450 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5852608064203273
 3593/3780 0.450 1.429 0.1 StandardScaler() 0 -> 0.5451295420906709
 3594/3780 0.450 1.429 0.1 MinMaxScaler()   0 -> 0.575974279332239
 3595/3780 0.450 1.429 0.31622776601683794 StandardScaler() 0 -> 0.5437190083885554
 3596/3780 0.450 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.562461335645695
 3597/3780 0.450 1.429 1.0 StandardScaler() 0 -> 0.5456854235879455
 3598/3780 0.450 1.429 1.0 MinMaxScaler()   0 -> 0.5545676102820226
 3599/3780 0.450 1.814 scale StandardScaler() 0 -> 0.5429598446198451
 3600/3780 0.450 1.814 scale MinMaxScaler()   0 -> 0.5451663685477421
 3601/3780 0.450 1.814 auto StandardScaler() 0 -> 0.5429598446198441
 3602/3780 0.450 1.814 auto MinMaxScaler()   0 -> 0.5651683505204349
 3603/3780 0.450 1.814 0.01 StandardScaler() 0 -> 0.5599809689984289
 3604/3780 0.450 1.814 0.01 MinMaxScaler()   0 -> 0.5899921448132934
 3605/3780 0.450 1.814 0.03162277660168379 StandardScaler() 0 -> 0.5534826234247373
 3606/3780 0.450 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5845269676651559
 3607/3780 0.450 1.814 0.1 StandardScaler() 0 -> 0.544444582441883
 3608/3780 0.450 1.814 0.1 MinMaxScaler()   0 -> 0.5744349394858913
 3609/3780 0.450 1.814 0.31622776601683794 StandardScaler() 0 -> 0.5462170506595041
 3610/3780 0.450 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.56223804117743
 3611/3780 0.450 1.814 1.0 StandardScaler() 0 -> 0.5481415240351052
 3612/3780 0.450 1.814 1.0 MinMaxScaler()   0 -> 0.5532362512709255
 3613/3780 0.450 2.302 scale StandardScaler() 0 -> 0.543435972157469
 3614/3780 0.450 2.302 scale MinMaxScaler()   0 -> 0.5451818089404356
 3615/3780 0.450 2.302 auto StandardScaler() 0 -> 0.543435972157469
 3616/3780 0.450 2.302 auto MinMaxScaler()   0 -> 0.5634472015694968
 3617/3780 0.450 2.302 0.01 StandardScaler() 0 -> 0.5590498526223914
 3618/3780 0.450 2.302 0.01 MinMaxScaler()   0 -> 0.5885940987705452
 3619/3780 0.450 2.302 0.03162277660168379 StandardScaler() 0 -> 0.5528114039785499
 3620/3780 0.450 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5839539984027388
 3621/3780 0.450 2.302 0.1 StandardScaler() 0 -> 0.5440580275274963
 3622/3780 0.450 2.302 0.1 MinMaxScaler()   0 -> 0.5726168313546206
 3623/3780 0.450 2.302 0.31622776601683794 StandardScaler() 0 -> 0.5492769944138809
 3624/3780 0.450 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.5619791385671993
 3625/3780 0.450 2.302 1.0 StandardScaler() 0 -> 0.551890832673083
 3626/3780 0.450 2.302 1.0 MinMaxScaler()   0 -> 0.5520039926995725
 3627/3780 0.450 2.921 scale StandardScaler() 0 -> 0.5448327068335826
 3628/3780 0.450 2.921 scale MinMaxScaler()   0 -> 0.5453445080271176
 3629/3780 0.450 2.921 auto StandardScaler() 0 -> 0.5448327068335818
 3630/3780 0.450 2.921 auto MinMaxScaler()   0 -> 0.562649151782996
 3631/3780 0.450 2.921 0.01 StandardScaler() 0 -> 0.5582784742541983
 3632/3780 0.450 2.921 0.01 MinMaxScaler()   0 -> 0.5876835647937084
 3633/3780 0.450 2.921 0.03162277660168379 StandardScaler() 0 -> 0.55141003479851
 3634/3780 0.450 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5830924162293294
 3635/3780 0.450 2.921 0.1 StandardScaler() 0 -> 0.544948277191511
 3636/3780 0.450 2.921 0.1 MinMaxScaler()   0 -> 0.5709097699111232
 3637/3780 0.450 2.921 0.31622776601683794 StandardScaler() 0 -> 0.5522207634362207
 3638/3780 0.450 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.5616094969515371
 3639/3780 0.450 2.921 1.0 StandardScaler() 0 -> 0.5583027640951658
 3640/3780 0.450 2.921 1.0 MinMaxScaler()   0 -> 0.5506050208224204
 3641/3780 0.450 3.707 scale StandardScaler() 0 -> 0.5475121180304344
 3642/3780 0.450 3.707 scale MinMaxScaler()   0 -> 0.5463052786150856
 3643/3780 0.450 3.707 auto StandardScaler() 0 -> 0.5475121180304345
 3644/3780 0.450 3.707 auto MinMaxScaler()   0 -> 0.5625455432038614
 3645/3780 0.450 3.707 0.01 StandardScaler() 0 -> 0.557885934432129
 3646/3780 0.450 3.707 0.01 MinMaxScaler()   0 -> 0.5864962927808874
 3647/3780 0.450 3.707 0.03162277660168379 StandardScaler() 0 -> 0.5498677955587042
 3648/3780 0.450 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5821651068821493
 3649/3780 0.450 3.707 0.1 StandardScaler() 0 -> 0.545668068186043
 3650/3780 0.450 3.707 0.1 MinMaxScaler()   0 -> 0.5689755510543778
 3651/3780 0.450 3.707 0.31622776601683794 StandardScaler() 0 -> 0.5552461351863421
 3652/3780 0.450 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.5603858819993486
 3653/3780 0.450 3.707 1.0 StandardScaler() 0 -> 0.5669598089015248
 3654/3780 0.450 3.707 1.0 MinMaxScaler()   0 -> 0.5496369867578661
 3655/3780 0.450 4.703 scale StandardScaler() 0 -> 0.5507583529664783
 3656/3780 0.450 4.703 scale MinMaxScaler()   0 -> 0.5481696833243955
 3657/3780 0.450 4.703 auto StandardScaler() 0 -> 0.550758352966476
 3658/3780 0.450 4.703 auto MinMaxScaler()   0 -> 0.562578143017766
 3659/3780 0.450 4.703 0.01 StandardScaler() 0 -> 0.5577449550379395
 3660/3780 0.450 4.703 0.01 MinMaxScaler()   0 -> 0.5859742876924959
 3661/3780 0.450 4.703 0.03162277660168379 StandardScaler() 0 -> 0.5487743889713185
 3662/3780 0.450 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5813272235623657
 3663/3780 0.450 4.703 0.1 StandardScaler() 0 -> 0.5459524441315995
 3664/3780 0.450 4.703 0.1 MinMaxScaler()   0 -> 0.5672200903347643
 3665/3780 0.450 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5582966248610676
 3666/3780 0.450 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.5602626421560705
 3667/3780 0.450 4.703 1.0 StandardScaler() 0 -> 0.5765509637933929
 3668/3780 0.450 4.703 1.0 MinMaxScaler()   0 -> 0.5480959376230574
 3669/3780 0.450 5.968 scale StandardScaler() 0 -> 0.5541618171714449
 3670/3780 0.450 5.968 scale MinMaxScaler()   0 -> 0.5510972598191096
 3671/3780 0.450 5.968 auto StandardScaler() 0 -> 0.5541618171714444
 3672/3780 0.450 5.968 auto MinMaxScaler()   0 -> 0.5623876917797236
 3673/3780 0.450 5.968 0.01 StandardScaler() 0 -> 0.5576871317984002
 3674/3780 0.450 5.968 0.01 MinMaxScaler()   0 -> 0.5856119522856846
 3675/3780 0.450 5.968 0.03162277660168379 StandardScaler() 0 -> 0.54800525116961
 3676/3780 0.450 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5803171468754794
 3677/3780 0.450 5.968 0.1 StandardScaler() 0 -> 0.5459600846497699
 3678/3780 0.450 5.968 0.1 MinMaxScaler()   0 -> 0.5658258335049898
 3679/3780 0.450 5.968 0.31622776601683794 StandardScaler() 0 -> 0.5617537443204453
 3680/3780 0.450 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.5597987781436313
 3681/3780 0.450 5.968 1.0 StandardScaler() 0 -> 0.5864802159420726
 3682/3780 0.450 5.968 1.0 MinMaxScaler()   0 -> 0.5473972566733772
 3683/3780 0.450 7.574 scale StandardScaler() 0 -> 0.5585573477543787
 3684/3780 0.450 7.574 scale MinMaxScaler()   0 -> 0.5552962204640912
 3685/3780 0.450 7.574 auto StandardScaler() 0 -> 0.5585573477543808
 3686/3780 0.450 7.574 auto MinMaxScaler()   0 -> 0.5618367025678537
 3687/3780 0.450 7.574 0.01 StandardScaler() 0 -> 0.5575386370152962
 3688/3780 0.450 7.574 0.01 MinMaxScaler()   0 -> 0.5852363797080313
 3689/3780 0.450 7.574 0.03162277660168379 StandardScaler() 0 -> 0.5468053523393452
 3690/3780 0.450 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5791585290433571
 3691/3780 0.450 7.574 0.1 StandardScaler() 0 -> 0.5467518750162409
 3692/3780 0.450 7.574 0.1 MinMaxScaler()   0 -> 0.5643406176649823
 3693/3780 0.450 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5656801788372338
 3694/3780 0.450 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.5594338720789104
 3695/3780 0.450 7.574 1.0 StandardScaler() 0 -> 0.5965234260091438
 3696/3780 0.450 7.574 1.0 MinMaxScaler()   0 -> 0.5465471271461279
 3697/3780 0.450 9.611 scale StandardScaler() 0 -> 0.563596183080897
 3698/3780 0.450 9.611 scale MinMaxScaler()   0 -> 0.5601847786504808
 3699/3780 0.450 9.611 auto StandardScaler() 0 -> 0.56359618308089
 3700/3780 0.450 9.611 auto MinMaxScaler()   0 -> 0.5608023421041537
 3701/3780 0.450 9.611 0.01 StandardScaler() 0 -> 0.5575135176422861
 3702/3780 0.450 9.611 0.01 MinMaxScaler()   0 -> 0.5847569733615323
 3703/3780 0.450 9.611 0.03162277660168379 StandardScaler() 0 -> 0.5457839394339453
 3704/3780 0.450 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5777425501722324
 3705/3780 0.450 9.611 0.1 StandardScaler() 0 -> 0.5474697934342169
 3706/3780 0.450 9.611 0.1 MinMaxScaler()   0 -> 0.5630520611174634
 3707/3780 0.450 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5713389915007326
 3708/3780 0.450 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.559140692312977
 3709/3780 0.450 9.611 1.0 StandardScaler() 0 -> 0.6078828981142764
 3710/3780 0.450 9.611 1.0 MinMaxScaler()   0 -> 0.5454796798942358
 3711/3780 0.450 12.196 scale StandardScaler() 0 -> 0.5688364802867946
 3712/3780 0.450 12.196 scale MinMaxScaler()   0 -> 0.5633108156906921
 3713/3780 0.450 12.196 auto StandardScaler() 0 -> 0.5688364802867922
 3714/3780 0.450 12.196 auto MinMaxScaler()   0 -> 0.560703373262545
 3715/3780 0.450 12.196 0.01 StandardScaler() 0 -> 0.5577484249085921
 3716/3780 0.450 12.196 0.01 MinMaxScaler()   0 -> 0.5843679014156061
 3717/3780 0.450 12.196 0.03162277660168379 StandardScaler() 0 -> 0.5444424885653952
 3718/3780 0.450 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5763211881019248
 3719/3780 0.450 12.196 0.1 StandardScaler() 0 -> 0.5494721337151272
 3720/3780 0.450 12.196 0.1 MinMaxScaler()   0 -> 0.5629785924373599
 3721/3780 0.450 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5763523225648216
 3722/3780 0.450 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.5583966134159758
 3723/3780 0.450 12.196 1.0 StandardScaler() 0 -> 0.6212727624959291
 3724/3780 0.450 12.196 1.0 MinMaxScaler()   0 -> 0.5444642368105876
 3725/3780 0.450 15.476 scale StandardScaler() 0 -> 0.5733033164494454
 3726/3780 0.450 15.476 scale MinMaxScaler()   0 -> 0.5674413268991221
 3727/3780 0.450 15.476 auto StandardScaler() 0 -> 0.5733033164494484
 3728/3780 0.450 15.476 auto MinMaxScaler()   0 -> 0.560249762236104
 3729/3780 0.450 15.476 0.01 StandardScaler() 0 -> 0.5573715892521881
 3730/3780 0.450 15.476 0.01 MinMaxScaler()   0 -> 0.5838899255723936
 3731/3780 0.450 15.476 0.03162277660168379 StandardScaler() 0 -> 0.5440157771947781
 3732/3780 0.450 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5750302845811173
 3733/3780 0.450 15.476 0.1 StandardScaler() 0 -> 0.5506146683204617
 3734/3780 0.450 15.476 0.1 MinMaxScaler()   0 -> 0.5626183428206283
 3735/3780 0.450 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5835099983581576
 3736/3780 0.450 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.5573324587345111
 3737/3780 0.450 15.476 1.0 StandardScaler() 0 -> 0.6346195598815724
 3738/3780 0.450 15.476 1.0 MinMaxScaler()   0 -> 0.5445718567553791
 3739/3780 0.450 19.638 scale StandardScaler() 0 -> 0.5768513151753445
 3740/3780 0.450 19.638 scale MinMaxScaler()   0 -> 0.5718423098258514
 3741/3780 0.450 19.638 auto StandardScaler() 0 -> 0.5768513151753395
 3742/3780 0.450 19.638 auto MinMaxScaler()   0 -> 0.5605291971185746
 3743/3780 0.450 19.638 0.01 StandardScaler() 0 -> 0.5565392702543411
 3744/3780 0.450 19.638 0.01 MinMaxScaler()   0 -> 0.5835454529880969
 3745/3780 0.450 19.638 0.03162277660168379 StandardScaler() 0 -> 0.5445337699513692
 3746/3780 0.450 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5734891612555417
 3747/3780 0.450 19.638 0.1 StandardScaler() 0 -> 0.5518713596880064
 3748/3780 0.450 19.638 0.1 MinMaxScaler()   0 -> 0.5627588265716162
 3749/3780 0.450 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5915550279499145
 3750/3780 0.450 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.5559715687008727
 3751/3780 0.450 19.638 1.0 StandardScaler() 0 -> 0.6496827685766331
 3752/3780 0.450 19.638 1.0 MinMaxScaler()   0 -> 0.5448203832104305
 3753/3780 0.450 24.920 scale StandardScaler() 0 -> 0.5812964962323778
 3754/3780 0.450 24.920 scale MinMaxScaler()   0 -> 0.5760132901474299
 3755/3780 0.450 24.920 auto StandardScaler() 0 -> 0.5812964962323844
 3756/3780 0.450 24.920 auto MinMaxScaler()   0 -> 0.5600924663338381
 3757/3780 0.450 24.920 0.01 StandardScaler() 0 -> 0.5563562094464509
 3758/3780 0.450 24.920 0.01 MinMaxScaler()   0 -> 0.583026614024709
 3759/3780 0.450 24.920 0.03162277660168379 StandardScaler() 0 -> 0.5452792994717074
 3760/3780 0.450 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5719282693246536
 3761/3780 0.450 24.920 0.1 StandardScaler() 0 -> 0.5548383667803573
 3762/3780 0.450 24.920 0.1 MinMaxScaler()   0 -> 0.5626808507204112
 3763/3780 0.450 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5992015403469909
 3764/3780 0.450 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.5549660583697814
 3765/3780 0.450 24.920 1.0 StandardScaler() 0 -> 0.6681777235388312
 3766/3780 0.450 24.920 1.0 MinMaxScaler()   0 -> 0.5453766598926791
 3767/3780 0.450 31.623 scale StandardScaler() 0 -> 0.5854843976370221
 3768/3780 0.450 31.623 scale MinMaxScaler()   0 -> 0.5799803336571
 3769/3780 0.450 31.623 auto StandardScaler() 0 -> 0.5854843976370324
 3770/3780 0.450 31.623 auto MinMaxScaler()   0 -> 0.5597309257843411
 3771/3780 0.450 31.623 0.01 StandardScaler() 0 -> 0.5556896605513687
 3772/3780 0.450 31.623 0.01 MinMaxScaler()   0 -> 0.5824064160054465
 3773/3780 0.450 31.623 0.03162277660168379 StandardScaler() 0 -> 0.545611606674775
 3774/3780 0.450 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5700133773261334
 3775/3780 0.450 31.623 0.1 StandardScaler() 0 -> 0.558663340475678
 3776/3780 0.450 31.623 0.1 MinMaxScaler()   0 -> 0.5623679896766564
 3777/3780 0.450 31.623 0.31622776601683794 StandardScaler() 0 -> 0.6084326378924043
 3778/3780 0.450 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.5543549248759133
 3779/3780 0.450 31.623 1.0 StandardScaler() 0 -> 0.68840512982178
 3780/3780 0.450 31.623 1.0 MinMaxScaler()   0 -> 0.5453299134882204
Time Taken 9650.848s

Hyperparametersuche SVR ohne Feature Selection

train_df, test_df = get_datasets(logarithm=True, feature_selection=False)

X_train = train_df.drop("quality",axis=1).values
X_test = test_df.drop("quality",axis=1).values

y_train = train_df["quality"].values
y_test = test_df["quality"].values

# Avaliable Kernels are {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’}
train_config = SupportVectorRegressor_train(X_train,y_train,cross_validation=True, kernel = "rbf", test_for_professor = test_for_professor)

df = pd.DataFrame.from_dict(train_config)
df = df.sort_values(by=['mse_val'])
if not test_for_professor:
    df.to_csv('results/train_conf_svr.csv',index=False)

    1/3780 0.050 0.032 scale StandardScaler() 0 -> 0.5417756875272429
    2/3780 0.050 0.032 scale MinMaxScaler()   0 -> 0.5316854466803687
    3/3780 0.050 0.032 auto StandardScaler() 0 -> 0.5417756875272429
    4/3780 0.050 0.032 auto MinMaxScaler()   0 -> 0.6885896949658922
    5/3780 0.050 0.032 0.01 StandardScaler() 0 -> 0.6350147002507263
    6/3780 0.050 0.032 0.01 MinMaxScaler()   0 -> 0.7391515282840602
    7/3780 0.050 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5631224105089
    8/3780 0.050 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.718194108362515
    9/3780 0.050 0.032 0.1 StandardScaler() 0 -> 0.5441232780771921
   10/3780 0.050 0.032 0.1 MinMaxScaler()   0 -> 0.6845473818306503
   11/3780 0.050 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6353615358334851
   12/3780 0.050 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6224281808356801
   13/3780 0.050 0.032 1.0 StandardScaler() 0 -> 0.7253388096625284
   14/3780 0.050 0.032 1.0 MinMaxScaler()   0 -> 0.5486988181982922
   15/3780 0.050 0.040 scale StandardScaler() 0 -> 0.5263334531021855
   16/3780 0.050 0.040 scale MinMaxScaler()   0 -> 0.5196418040258937
   17/3780 0.050 0.040 auto StandardScaler() 0 -> 0.5263334531021854
   18/3780 0.050 0.040 auto MinMaxScaler()   0 -> 0.6787833467611174
   19/3780 0.050 0.040 0.01 StandardScaler() 0 -> 0.6166508470360693
   20/3780 0.050 0.040 0.01 MinMaxScaler()   0 -> 0.7347376569364087
   21/3780 0.050 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5439047722218814
   22/3780 0.050 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7127844895736883
   23/3780 0.050 0.040 0.1 StandardScaler() 0 -> 0.5284501945375598
   24/3780 0.050 0.040 0.1 MinMaxScaler()   0 -> 0.6744470358266433
   25/3780 0.050 0.040 0.31622776601683794 StandardScaler() 0 -> 0.6146516921075547
   26/3780 0.050 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.6015852109713965
   27/3780 0.050 0.040 1.0 StandardScaler() 0 -> 0.7174509118934788
   28/3780 0.050 0.040 1.0 MinMaxScaler()   0 -> 0.5326029352638648
   29/3780 0.050 0.051 scale StandardScaler() 0 -> 0.5162382271808187
   30/3780 0.050 0.051 scale MinMaxScaler()   0 -> 0.5120580774607112
   31/3780 0.050 0.051 auto StandardScaler() 0 -> 0.5162382271808187
   32/3780 0.050 0.051 auto MinMaxScaler()   0 -> 0.6668243305599088
   33/3780 0.050 0.051 0.01 StandardScaler() 0 -> 0.5969668583056813
   34/3780 0.050 0.051 0.01 MinMaxScaler()   0 -> 0.7302414641296567
   35/3780 0.050 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5301836430561943
   36/3780 0.050 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7064988571141138
   37/3780 0.050 0.051 0.1 StandardScaler() 0 -> 0.5179871370063399
   38/3780 0.050 0.051 0.1 MinMaxScaler()   0 -> 0.6614633230012762
   39/3780 0.050 0.051 0.31622776601683794 StandardScaler() 0 -> 0.592972507735277
   40/3780 0.050 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5810810904142143
   41/3780 0.050 0.051 1.0 StandardScaler() 0 -> 0.7078212427052307
   42/3780 0.050 0.051 1.0 MinMaxScaler()   0 -> 0.5207333864933087
   43/3780 0.050 0.065 scale StandardScaler() 0 -> 0.5092032307798616
   44/3780 0.050 0.065 scale MinMaxScaler()   0 -> 0.5065241217183292
   45/3780 0.050 0.065 auto StandardScaler() 0 -> 0.5092032307798616
   46/3780 0.050 0.065 auto MinMaxScaler()   0 -> 0.6528277491908262
   47/3780 0.050 0.065 0.01 StandardScaler() 0 -> 0.5773979502622207
   48/3780 0.050 0.065 0.01 MinMaxScaler()   0 -> 0.7260164027080508
   49/3780 0.050 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5190697167498945
   50/3780 0.050 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6994210269909642
   51/3780 0.050 0.065 0.1 StandardScaler() 0 -> 0.5104755744635244
   52/3780 0.050 0.065 0.1 MinMaxScaler()   0 -> 0.6467398892407229
   53/3780 0.050 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5722785291129089
   54/3780 0.050 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5626973752161925
   55/3780 0.050 0.065 1.0 StandardScaler() 0 -> 0.696267961050013
   56/3780 0.050 0.065 1.0 MinMaxScaler()   0 -> 0.5136537986189046
   57/3780 0.050 0.082 scale StandardScaler() 0 -> 0.5038816496768466
   58/3780 0.050 0.082 scale MinMaxScaler()   0 -> 0.50263663839903
   59/3780 0.050 0.082 auto StandardScaler() 0 -> 0.5038816496768467
   60/3780 0.050 0.082 auto MinMaxScaler()   0 -> 0.6365150513262353
   61/3780 0.050 0.082 0.01 StandardScaler() 0 -> 0.5614692607643522
   62/3780 0.050 0.082 0.01 MinMaxScaler()   0 -> 0.7220321888520793
   63/3780 0.050 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5125464825253718
   64/3780 0.050 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6908915698193371
   65/3780 0.050 0.082 0.1 StandardScaler() 0 -> 0.5047121779024161
   66/3780 0.050 0.082 0.1 MinMaxScaler()   0 -> 0.6298038621118833
   67/3780 0.050 0.082 0.31622776601683794 StandardScaler() 0 -> 0.554023275520302
   68/3780 0.050 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5502978965778179
   69/3780 0.050 0.082 1.0 StandardScaler() 0 -> 0.6825440351963438
   70/3780 0.050 0.082 1.0 MinMaxScaler()   0 -> 0.5088836689640638
   71/3780 0.050 0.104 scale StandardScaler() 0 -> 0.49888679041628153
   72/3780 0.050 0.104 scale MinMaxScaler()   0 -> 0.49930985082825036
   73/3780 0.050 0.104 auto StandardScaler() 0 -> 0.49888679041628153
   74/3780 0.050 0.104 auto MinMaxScaler()   0 -> 0.6179789583771612
   75/3780 0.050 0.104 0.01 StandardScaler() 0 -> 0.5509082096431364
   76/3780 0.050 0.104 0.01 MinMaxScaler()   0 -> 0.7168559986950563
   77/3780 0.050 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5080716448577801
   78/3780 0.050 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6814652148256535
   79/3780 0.050 0.104 0.1 StandardScaler() 0 -> 0.4992027549485052
   80/3780 0.050 0.104 0.1 MinMaxScaler()   0 -> 0.6102798615659029
   81/3780 0.050 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5392897364138621
   82/3780 0.050 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5405055127570358
   83/3780 0.050 0.104 1.0 StandardScaler() 0 -> 0.666356031607992
   84/3780 0.050 0.104 1.0 MinMaxScaler()   0 -> 0.5058293695158047
   85/3780 0.050 0.132 scale StandardScaler() 0 -> 0.49488211134282656
   86/3780 0.050 0.132 scale MinMaxScaler()   0 -> 0.49646287392214167
   87/3780 0.050 0.132 auto StandardScaler() 0 -> 0.49488211134282656
   88/3780 0.050 0.132 auto MinMaxScaler()   0 -> 0.5985454568721583
   89/3780 0.050 0.132 0.01 StandardScaler() 0 -> 0.541368176523545
   90/3780 0.050 0.132 0.01 MinMaxScaler()   0 -> 0.7114262400708505
   91/3780 0.050 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5048570327170535
   92/3780 0.050 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6706551848866065
   93/3780 0.050 0.132 0.1 StandardScaler() 0 -> 0.49567594070777893
   94/3780 0.050 0.132 0.1 MinMaxScaler()   0 -> 0.591140319534765
   95/3780 0.050 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5258257942115764
   96/3780 0.050 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5325298337835055
   97/3780 0.050 0.132 1.0 StandardScaler() 0 -> 0.6478962649359502
   98/3780 0.050 0.132 1.0 MinMaxScaler()   0 -> 0.5032112071571574
   99/3780 0.050 0.168 scale StandardScaler() 0 -> 0.492295861901691
  100/3780 0.050 0.168 scale MinMaxScaler()   0 -> 0.49376277847333044
  101/3780 0.050 0.168 auto StandardScaler() 0 -> 0.492295861901691
  102/3780 0.050 0.168 auto MinMaxScaler()   0 -> 0.5806158811946478
  103/3780 0.050 0.168 0.01 StandardScaler() 0 -> 0.5327204616599012
  104/3780 0.050 0.168 0.01 MinMaxScaler()   0 -> 0.7049188025073384
  105/3780 0.050 0.168 0.03162277660168379 StandardScaler() 0 -> 0.502021290638413
  106/3780 0.050 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6574873645649861
  107/3780 0.050 0.168 0.1 StandardScaler() 0 -> 0.49241034010500423
  108/3780 0.050 0.168 0.1 MinMaxScaler()   0 -> 0.573875354654806
  109/3780 0.050 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5138870635576166
  110/3780 0.050 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5262788926098371
  111/3780 0.050 0.168 1.0 StandardScaler() 0 -> 0.6277329172592755
  112/3780 0.050 0.168 1.0 MinMaxScaler()   0 -> 0.5007739804432949
  113/3780 0.050 0.213 scale StandardScaler() 0 -> 0.4892660226583924
  114/3780 0.050 0.213 scale MinMaxScaler()   0 -> 0.4911719670123253
  115/3780 0.050 0.213 auto StandardScaler() 0 -> 0.48926602265839253
  116/3780 0.050 0.213 auto MinMaxScaler()   0 -> 0.5663611807920234
  117/3780 0.050 0.213 0.01 StandardScaler() 0 -> 0.5266879553950335
  118/3780 0.050 0.213 0.01 MinMaxScaler()   0 -> 0.6973722061941624
  119/3780 0.050 0.213 0.03162277660168379 StandardScaler() 0 -> 0.49983495565665176
  120/3780 0.050 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6420673178814529
  121/3780 0.050 0.213 0.1 StandardScaler() 0 -> 0.4890335277747819
  122/3780 0.050 0.213 0.1 MinMaxScaler()   0 -> 0.5617929574669587
  123/3780 0.050 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5029203046402447
  124/3780 0.050 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5212202938973468
  125/3780 0.050 0.213 1.0 StandardScaler() 0 -> 0.6060774894963301
  126/3780 0.050 0.213 1.0 MinMaxScaler()   0 -> 0.4989068115346005
  127/3780 0.050 0.270 scale StandardScaler() 0 -> 0.4860964115696027
  128/3780 0.050 0.270 scale MinMaxScaler()   0 -> 0.48890344068106245
  129/3780 0.050 0.270 auto StandardScaler() 0 -> 0.4860964115696027
  130/3780 0.050 0.270 auto MinMaxScaler()   0 -> 0.5573403006377406
  131/3780 0.050 0.270 0.01 StandardScaler() 0 -> 0.5213812241348851
  132/3780 0.050 0.270 0.01 MinMaxScaler()   0 -> 0.6889733147160456
  133/3780 0.050 0.270 0.03162277660168379 StandardScaler() 0 -> 0.4978116136655419
  134/3780 0.050 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6245836362083669
  135/3780 0.050 0.270 0.1 StandardScaler() 0 -> 0.4857316047525618
  136/3780 0.050 0.270 0.1 MinMaxScaler()   0 -> 0.5540794087875608
  137/3780 0.050 0.270 0.31622776601683794 StandardScaler() 0 -> 0.49335358402182344
  138/3780 0.050 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5170533979395359
  139/3780 0.050 0.270 1.0 StandardScaler() 0 -> 0.5838847005965058
  140/3780 0.050 0.270 1.0 MinMaxScaler()   0 -> 0.4968198607683889
  141/3780 0.050 0.342 scale StandardScaler() 0 -> 0.48385822884063856
  142/3780 0.050 0.342 scale MinMaxScaler()   0 -> 0.4869433429017489
  143/3780 0.050 0.342 auto StandardScaler() 0 -> 0.48385822884063856
  144/3780 0.050 0.342 auto MinMaxScaler()   0 -> 0.5515557884715062
  145/3780 0.050 0.342 0.01 StandardScaler() 0 -> 0.516544002379184
  146/3780 0.050 0.342 0.01 MinMaxScaler()   0 -> 0.6795926604200381
  147/3780 0.050 0.342 0.03162277660168379 StandardScaler() 0 -> 0.4963334429012441
  148/3780 0.050 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.6049582334340049
  149/3780 0.050 0.342 0.1 StandardScaler() 0 -> 0.48313689887882555
  150/3780 0.050 0.342 0.1 MinMaxScaler()   0 -> 0.5489796370346286
  151/3780 0.050 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4849997722637254
  152/3780 0.050 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5137689696145323
  153/3780 0.050 0.342 1.0 StandardScaler() 0 -> 0.5623517055705887
  154/3780 0.050 0.342 1.0 MinMaxScaler()   0 -> 0.4959316658699697
  155/3780 0.050 0.434 scale StandardScaler() 0 -> 0.48177610507202245
  156/3780 0.050 0.434 scale MinMaxScaler()   0 -> 0.48526484728382585
  157/3780 0.050 0.434 auto StandardScaler() 0 -> 0.4817761050720226
  158/3780 0.050 0.434 auto MinMaxScaler()   0 -> 0.5469813430572553
  159/3780 0.050 0.434 0.01 StandardScaler() 0 -> 0.512971667011457
  160/3780 0.050 0.434 0.01 MinMaxScaler()   0 -> 0.6679435189832361
  161/3780 0.050 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49485492292970995
  162/3780 0.050 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.587109917228117
  163/3780 0.050 0.434 0.1 StandardScaler() 0 -> 0.4809124922467438
  164/3780 0.050 0.434 0.1 MinMaxScaler()   0 -> 0.5448684733105802
  165/3780 0.050 0.434 0.31622776601683794 StandardScaler() 0 -> 0.4782819313842024
  166/3780 0.050 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5102114861923459
  167/3780 0.050 0.434 1.0 StandardScaler() 0 -> 0.5418592835663218
  168/3780 0.050 0.434 1.0 MinMaxScaler()   0 -> 0.49458426601963873
  169/3780 0.050 0.551 scale StandardScaler() 0 -> 0.4799449362490213
  170/3780 0.050 0.551 scale MinMaxScaler()   0 -> 0.48374173537122384
  171/3780 0.050 0.551 auto StandardScaler() 0 -> 0.47994493624902135
  172/3780 0.050 0.551 auto MinMaxScaler()   0 -> 0.5431320238010807
  173/3780 0.050 0.551 0.01 StandardScaler() 0 -> 0.5094728651930552
  174/3780 0.050 0.551 0.01 MinMaxScaler()   0 -> 0.6545530833107155
  175/3780 0.050 0.551 0.03162277660168379 StandardScaler() 0 -> 0.49369689761301633
  176/3780 0.050 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5719441852266901
  177/3780 0.050 0.551 0.1 StandardScaler() 0 -> 0.47933249291214786
  178/3780 0.050 0.551 0.1 MinMaxScaler()   0 -> 0.5407625155705016
  179/3780 0.050 0.551 0.31622776601683794 StandardScaler() 0 -> 0.47313628605332303
  180/3780 0.050 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5076364355918274
  181/3780 0.050 0.551 1.0 StandardScaler() 0 -> 0.5234293545723808
  182/3780 0.050 0.551 1.0 MinMaxScaler()   0 -> 0.49310810644910735
  183/3780 0.050 0.700 scale StandardScaler() 0 -> 0.4786954695876518
  184/3780 0.050 0.700 scale MinMaxScaler()   0 -> 0.48178947340216394
  185/3780 0.050 0.700 auto StandardScaler() 0 -> 0.47869546958765213
  186/3780 0.050 0.700 auto MinMaxScaler()   0 -> 0.5397841965442726
  187/3780 0.050 0.700 0.01 StandardScaler() 0 -> 0.5071613031173042
  188/3780 0.050 0.700 0.01 MinMaxScaler()   0 -> 0.638442615188342
  189/3780 0.050 0.700 0.03162277660168379 StandardScaler() 0 -> 0.4923139843359075
  190/3780 0.050 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5629119247031708
  191/3780 0.050 0.700 0.1 StandardScaler() 0 -> 0.47783891719753585
  192/3780 0.050 0.700 0.1 MinMaxScaler()   0 -> 0.5375551447755559
  193/3780 0.050 0.700 0.31622776601683794 StandardScaler() 0 -> 0.4700207967104442
  194/3780 0.050 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5054551329735592
  195/3780 0.050 0.700 1.0 StandardScaler() 0 -> 0.5079790435623985
  196/3780 0.050 0.700 1.0 MinMaxScaler()   0 -> 0.4920508358307367
  197/3780 0.050 0.888 scale StandardScaler() 0 -> 0.4778714596467147
  198/3780 0.050 0.888 scale MinMaxScaler()   0 -> 0.47989753989242634
  199/3780 0.050 0.888 auto StandardScaler() 0 -> 0.4778714596467144
  200/3780 0.050 0.888 auto MinMaxScaler()   0 -> 0.5364895773933279
  201/3780 0.050 0.888 0.01 StandardScaler() 0 -> 0.5047913278893797
  202/3780 0.050 0.888 0.01 MinMaxScaler()   0 -> 0.6201396970380224
  203/3780 0.050 0.888 0.03162277660168379 StandardScaler() 0 -> 0.49077376218879093
  204/3780 0.050 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5576608021590969
  205/3780 0.050 0.888 0.1 StandardScaler() 0 -> 0.47695470805366186
  206/3780 0.050 0.888 0.1 MinMaxScaler()   0 -> 0.5335801570674343
  207/3780 0.050 0.888 0.31622776601683794 StandardScaler() 0 -> 0.46912577428776414
  208/3780 0.050 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5040770951262662
  209/3780 0.050 0.888 1.0 StandardScaler() 0 -> 0.49589383438322177
  210/3780 0.050 0.888 1.0 MinMaxScaler()   0 -> 0.4907098068890572
  211/3780 0.050 1.126 scale StandardScaler() 0 -> 0.47720075255999067
  212/3780 0.050 1.126 scale MinMaxScaler()   0 -> 0.47883896908678314
  213/3780 0.050 1.126 auto StandardScaler() 0 -> 0.47720075255999106
  214/3780 0.050 1.126 auto MinMaxScaler()   0 -> 0.5326852273930224
  215/3780 0.050 1.126 0.01 StandardScaler() 0 -> 0.5029145358575807
  216/3780 0.050 1.126 0.01 MinMaxScaler()   0 -> 0.6010029349504362
  217/3780 0.050 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48978873346376456
  218/3780 0.050 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5546354612895025
  219/3780 0.050 1.126 0.1 StandardScaler() 0 -> 0.47587606221385537
  220/3780 0.050 1.126 0.1 MinMaxScaler()   0 -> 0.5294591644109383
  221/3780 0.050 1.126 0.31622776601683794 StandardScaler() 0 -> 0.4696320929197319
  222/3780 0.050 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.502804050737248
  223/3780 0.050 1.126 1.0 StandardScaler() 0 -> 0.4894757004146768
  224/3780 0.050 1.126 1.0 MinMaxScaler()   0 -> 0.48897683100023115
  225/3780 0.050 1.429 scale StandardScaler() 0 -> 0.47634177825847485
  226/3780 0.050 1.429 scale MinMaxScaler()   0 -> 0.47778981547033944
  227/3780 0.050 1.429 auto StandardScaler() 0 -> 0.47634177825847407
  228/3780 0.050 1.429 auto MinMaxScaler()   0 -> 0.5288569619441066
  229/3780 0.050 1.429 0.01 StandardScaler() 0 -> 0.5022616865900765
  230/3780 0.050 1.429 0.01 MinMaxScaler()   0 -> 0.5842872046740216
  231/3780 0.050 1.429 0.03162277660168379 StandardScaler() 0 -> 0.4890564979395216
  232/3780 0.050 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5520397946219174
  233/3780 0.050 1.429 0.1 StandardScaler() 0 -> 0.4755577913669315
  234/3780 0.050 1.429 0.1 MinMaxScaler()   0 -> 0.5260786887078298
  235/3780 0.050 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4717815460264254
  236/3780 0.050 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5017900982656133
  237/3780 0.050 1.429 1.0 StandardScaler() 0 -> 0.4863709684436634
  238/3780 0.050 1.429 1.0 MinMaxScaler()   0 -> 0.48815063122999786
  239/3780 0.050 1.814 scale StandardScaler() 0 -> 0.47646901294680616
  240/3780 0.050 1.814 scale MinMaxScaler()   0 -> 0.4770957841241814
  241/3780 0.050 1.814 auto StandardScaler() 0 -> 0.47646901294680527
  242/3780 0.050 1.814 auto MinMaxScaler()   0 -> 0.525197630237999
  243/3780 0.050 1.814 0.01 StandardScaler() 0 -> 0.5014903158340293
  244/3780 0.050 1.814 0.01 MinMaxScaler()   0 -> 0.5709990287764996
  245/3780 0.050 1.814 0.03162277660168379 StandardScaler() 0 -> 0.4881950704728715
  246/3780 0.050 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5498530896482993
  247/3780 0.050 1.814 0.1 StandardScaler() 0 -> 0.4754206218591659
  248/3780 0.050 1.814 0.1 MinMaxScaler()   0 -> 0.5221301491959884
  249/3780 0.050 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4752956319533748
  250/3780 0.050 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.500420929565054
  251/3780 0.050 1.814 1.0 StandardScaler() 0 -> 0.48492524924617136
  252/3780 0.050 1.814 1.0 MinMaxScaler()   0 -> 0.4880755460672592
  253/3780 0.050 2.302 scale StandardScaler() 0 -> 0.47649238229644136
  254/3780 0.050 2.302 scale MinMaxScaler()   0 -> 0.4771826246825434
  255/3780 0.050 2.302 auto StandardScaler() 0 -> 0.4764923822964424
  256/3780 0.050 2.302 auto MinMaxScaler()   0 -> 0.5212380040120576
  257/3780 0.050 2.302 0.01 StandardScaler() 0 -> 0.5006761850282923
  258/3780 0.050 2.302 0.01 MinMaxScaler()   0 -> 0.5630325114017899
  259/3780 0.050 2.302 0.03162277660168379 StandardScaler() 0 -> 0.4878476246134648
  260/3780 0.050 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5481970078206553
  261/3780 0.050 2.302 0.1 StandardScaler() 0 -> 0.4763388934483444
  262/3780 0.050 2.302 0.1 MinMaxScaler()   0 -> 0.518071598481016
  263/3780 0.050 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4795150945101832
  264/3780 0.050 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.49943794628693383
  265/3780 0.050 2.302 1.0 StandardScaler() 0 -> 0.48513299530352133
  266/3780 0.050 2.302 1.0 MinMaxScaler()   0 -> 0.4876001444026205
  267/3780 0.050 2.921 scale StandardScaler() 0 -> 0.478019675239727
  268/3780 0.050 2.921 scale MinMaxScaler()   0 -> 0.47727206359135543
  269/3780 0.050 2.921 auto StandardScaler() 0 -> 0.4780196752397275
  270/3780 0.050 2.921 auto MinMaxScaler()   0 -> 0.5174477932467232
  271/3780 0.050 2.921 0.01 StandardScaler() 0 -> 0.5004615422258866
  272/3780 0.050 2.921 0.01 MinMaxScaler()   0 -> 0.5588793853980926
  273/3780 0.050 2.921 0.03162277660168379 StandardScaler() 0 -> 0.48716314458352855
  274/3780 0.050 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.545984528685402
  275/3780 0.050 2.921 0.1 StandardScaler() 0 -> 0.47919464326697586
  276/3780 0.050 2.921 0.1 MinMaxScaler()   0 -> 0.5146842574641702
  277/3780 0.050 2.921 0.31622776601683794 StandardScaler() 0 -> 0.48459837009863
  278/3780 0.050 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49871824135643666
  279/3780 0.050 2.921 1.0 StandardScaler() 0 -> 0.4848980410922332
  280/3780 0.050 2.921 1.0 MinMaxScaler()   0 -> 0.4866349749516988
  281/3780 0.050 3.707 scale StandardScaler() 0 -> 0.4812996130000697
  282/3780 0.050 3.707 scale MinMaxScaler()   0 -> 0.4782042075752319
  283/3780 0.050 3.707 auto StandardScaler() 0 -> 0.48129961300007024
  284/3780 0.050 3.707 auto MinMaxScaler()   0 -> 0.5139152525877925
  285/3780 0.050 3.707 0.01 StandardScaler() 0 -> 0.499945962224573
  286/3780 0.050 3.707 0.01 MinMaxScaler()   0 -> 0.5564037078583023
  287/3780 0.050 3.707 0.03162277660168379 StandardScaler() 0 -> 0.4860680965644894
  288/3780 0.050 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.543758201930447
  289/3780 0.050 3.707 0.1 StandardScaler() 0 -> 0.48259161988019117
  290/3780 0.050 3.707 0.1 MinMaxScaler()   0 -> 0.5116325741221334
  291/3780 0.050 3.707 0.31622776601683794 StandardScaler() 0 -> 0.4925025956853694
  292/3780 0.050 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4978137167373058
  293/3780 0.050 3.707 1.0 StandardScaler() 0 -> 0.4848728847231576
  294/3780 0.050 3.707 1.0 MinMaxScaler()   0 -> 0.48581015046631765
  295/3780 0.050 4.703 scale StandardScaler() 0 -> 0.48521539374910905
  296/3780 0.050 4.703 scale MinMaxScaler()   0 -> 0.4811741458787437
  297/3780 0.050 4.703 auto StandardScaler() 0 -> 0.4852153937491095
  298/3780 0.050 4.703 auto MinMaxScaler()   0 -> 0.5110332596787349
  299/3780 0.050 4.703 0.01 StandardScaler() 0 -> 0.4992190776020074
  300/3780 0.050 4.703 0.01 MinMaxScaler()   0 -> 0.5549824462117194
  301/3780 0.050 4.703 0.03162277660168379 StandardScaler() 0 -> 0.4848169727475737
  302/3780 0.050 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5411745934634883
  303/3780 0.050 4.703 0.1 StandardScaler() 0 -> 0.4872440358128925
  304/3780 0.050 4.703 0.1 MinMaxScaler()   0 -> 0.5090589684899521
  305/3780 0.050 4.703 0.31622776601683794 StandardScaler() 0 -> 0.5000375543538588
  306/3780 0.050 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.497232443619735
  307/3780 0.050 4.703 1.0 StandardScaler() 0 -> 0.48502024181466735
  308/3780 0.050 4.703 1.0 MinMaxScaler()   0 -> 0.4846776497686763
  309/3780 0.050 5.968 scale StandardScaler() 0 -> 0.48999212063064346
  310/3780 0.050 5.968 scale MinMaxScaler()   0 -> 0.48474594683656913
  311/3780 0.050 5.968 auto StandardScaler() 0 -> 0.4899921206306464
  312/3780 0.050 5.968 auto MinMaxScaler()   0 -> 0.5087776741373345
  313/3780 0.050 5.968 0.01 StandardScaler() 0 -> 0.4988061011664137
  314/3780 0.050 5.968 0.01 MinMaxScaler()   0 -> 0.5536405793177864
  315/3780 0.050 5.968 0.03162277660168379 StandardScaler() 0 -> 0.485035790471791
  316/3780 0.050 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5383253081512622
  317/3780 0.050 5.968 0.1 StandardScaler() 0 -> 0.4932271983268965
  318/3780 0.050 5.968 0.1 MinMaxScaler()   0 -> 0.5074415997945749
  319/3780 0.050 5.968 0.31622776601683794 StandardScaler() 0 -> 0.507631838193443
  320/3780 0.050 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.49709945125912536
  321/3780 0.050 5.968 1.0 StandardScaler() 0 -> 0.48520301448439307
  322/3780 0.050 5.968 1.0 MinMaxScaler()   0 -> 0.4842614879143099
  323/3780 0.050 7.574 scale StandardScaler() 0 -> 0.4961081060922649
  324/3780 0.050 7.574 scale MinMaxScaler()   0 -> 0.48905062250807685
  325/3780 0.050 7.574 auto StandardScaler() 0 -> 0.49610810609225925
  326/3780 0.050 7.574 auto MinMaxScaler()   0 -> 0.5068539780514866
  327/3780 0.050 7.574 0.01 StandardScaler() 0 -> 0.49808096903472293
  328/3780 0.050 7.574 0.01 MinMaxScaler()   0 -> 0.5526043348302329
  329/3780 0.050 7.574 0.03162277660168379 StandardScaler() 0 -> 0.48534888838283247
  330/3780 0.050 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5352865429219814
  331/3780 0.050 7.574 0.1 StandardScaler() 0 -> 0.5011344679263556
  332/3780 0.050 7.574 0.1 MinMaxScaler()   0 -> 0.5057645096682649
  333/3780 0.050 7.574 0.31622776601683794 StandardScaler() 0 -> 0.517289487441889
  334/3780 0.050 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.4963126440013414
  335/3780 0.050 7.574 1.0 StandardScaler() 0 -> 0.4854246251199867
  336/3780 0.050 7.574 1.0 MinMaxScaler()   0 -> 0.4841303242837353
  337/3780 0.050 9.611 scale StandardScaler() 0 -> 0.5043105042809893
  338/3780 0.050 9.611 scale MinMaxScaler()   0 -> 0.49353614472365775
  339/3780 0.050 9.611 auto StandardScaler() 0 -> 0.5043105042809887
  340/3780 0.050 9.611 auto MinMaxScaler()   0 -> 0.5057345750477822
  341/3780 0.050 9.611 0.01 StandardScaler() 0 -> 0.49699156153007956
  342/3780 0.050 9.611 0.01 MinMaxScaler()   0 -> 0.5516310642039585
  343/3780 0.050 9.611 0.03162277660168379 StandardScaler() 0 -> 0.48520071793994957
  344/3780 0.050 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5315806623797162
  345/3780 0.050 9.611 0.1 StandardScaler() 0 -> 0.5100425377808996
  346/3780 0.050 9.611 0.1 MinMaxScaler()   0 -> 0.5049315816389299
  347/3780 0.050 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5286606464268088
  348/3780 0.050 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4954178580184803
  349/3780 0.050 9.611 1.0 StandardScaler() 0 -> 0.48554245751309305
  350/3780 0.050 9.611 1.0 MinMaxScaler()   0 -> 0.48463820127628116
  351/3780 0.050 12.196 scale StandardScaler() 0 -> 0.5135174856761849
  352/3780 0.050 12.196 scale MinMaxScaler()   0 -> 0.4987570368688566
  353/3780 0.050 12.196 auto StandardScaler() 0 -> 0.5135174856761815
  354/3780 0.050 12.196 auto MinMaxScaler()   0 -> 0.5049317668273224
  355/3780 0.050 12.196 0.01 StandardScaler() 0 -> 0.49623540368097335
  356/3780 0.050 12.196 0.01 MinMaxScaler()   0 -> 0.5507951426911254
  357/3780 0.050 12.196 0.03162277660168379 StandardScaler() 0 -> 0.4854994176002176
  358/3780 0.050 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5281349523678761
  359/3780 0.050 12.196 0.1 StandardScaler() 0 -> 0.5218846521742616
  360/3780 0.050 12.196 0.1 MinMaxScaler()   0 -> 0.5039671865971967
  361/3780 0.050 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5386867523312588
  362/3780 0.050 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4948051168766044
  363/3780 0.050 12.196 1.0 StandardScaler() 0 -> 0.48555008895269
  364/3780 0.050 12.196 1.0 MinMaxScaler()   0 -> 0.48631183318258436
  365/3780 0.050 15.476 scale StandardScaler() 0 -> 0.5261330657921803
  366/3780 0.050 15.476 scale MinMaxScaler()   0 -> 0.5048631046516325
  367/3780 0.050 15.476 auto StandardScaler() 0 -> 0.5261330657921771
  368/3780 0.050 15.476 auto MinMaxScaler()   0 -> 0.5037421719367496
  369/3780 0.050 15.476 0.01 StandardScaler() 0 -> 0.49581891882462537
  370/3780 0.050 15.476 0.01 MinMaxScaler()   0 -> 0.5497539746560826
  371/3780 0.050 15.476 0.03162277660168379 StandardScaler() 0 -> 0.48652268317906944
  372/3780 0.050 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5243793403164361
  373/3780 0.050 15.476 0.1 StandardScaler() 0 -> 0.5379229587999604
  374/3780 0.050 15.476 0.1 MinMaxScaler()   0 -> 0.5030756900865695
  375/3780 0.050 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5461154857546763
  376/3780 0.050 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.4938970838352604
  377/3780 0.050 15.476 1.0 StandardScaler() 0 -> 0.48555043132407333
  378/3780 0.050 15.476 1.0 MinMaxScaler()   0 -> 0.48807007584676526
  379/3780 0.050 19.638 scale StandardScaler() 0 -> 0.5430028304744362
  380/3780 0.050 19.638 scale MinMaxScaler()   0 -> 0.5136643761834065
  381/3780 0.050 19.638 auto StandardScaler() 0 -> 0.5430028304744393
  382/3780 0.050 19.638 auto MinMaxScaler()   0 -> 0.5031589702683745
  383/3780 0.050 19.638 0.01 StandardScaler() 0 -> 0.4953528656361381
  384/3780 0.050 19.638 0.01 MinMaxScaler()   0 -> 0.5484255766307119
  385/3780 0.050 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4883734468229941
  386/3780 0.050 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5203000303497458
  387/3780 0.050 19.638 0.1 StandardScaler() 0 -> 0.5563882628897346
  388/3780 0.050 19.638 0.1 MinMaxScaler()   0 -> 0.5026752263147879
  389/3780 0.050 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5521057438396655
  390/3780 0.050 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.49321973393806456
  391/3780 0.050 19.638 1.0 StandardScaler() 0 -> 0.48555356606842137
  392/3780 0.050 19.638 1.0 MinMaxScaler()   0 -> 0.4891875397164016
  393/3780 0.050 24.920 scale StandardScaler() 0 -> 0.5623300604968581
  394/3780 0.050 24.920 scale MinMaxScaler()   0 -> 0.5265253262126989
  395/3780 0.050 24.920 auto StandardScaler() 0 -> 0.5623300604968589
  396/3780 0.050 24.920 auto MinMaxScaler()   0 -> 0.502831158840385
  397/3780 0.050 24.920 0.01 StandardScaler() 0 -> 0.4948928955607215
  398/3780 0.050 24.920 0.01 MinMaxScaler()   0 -> 0.546792402098771
  399/3780 0.050 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4917208463329721
  400/3780 0.050 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5166959429002883
  401/3780 0.050 24.920 0.1 StandardScaler() 0 -> 0.5799347684544297
  402/3780 0.050 24.920 0.1 MinMaxScaler()   0 -> 0.5022821885693142
  403/3780 0.050 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5575686046572264
  404/3780 0.050 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.49265900341348406
  405/3780 0.050 24.920 1.0 StandardScaler() 0 -> 0.48555356606842137
  406/3780 0.050 24.920 1.0 MinMaxScaler()   0 -> 0.4902652158480758
  407/3780 0.050 31.623 scale StandardScaler() 0 -> 0.5864074035257794
  408/3780 0.050 31.623 scale MinMaxScaler()   0 -> 0.5418619513510174
  409/3780 0.050 31.623 auto StandardScaler() 0 -> 0.5864074035257774
  410/3780 0.050 31.623 auto MinMaxScaler()   0 -> 0.5023839230174686
  411/3780 0.050 31.623 0.01 StandardScaler() 0 -> 0.49419785614481654
  412/3780 0.050 31.623 0.01 MinMaxScaler()   0 -> 0.5448047995593243
  413/3780 0.050 31.623 0.03162277660168379 StandardScaler() 0 -> 0.49435021650512895
  414/3780 0.050 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5135466672393257
  415/3780 0.050 31.623 0.1 StandardScaler() 0 -> 0.6047210398039082
  416/3780 0.050 31.623 0.1 MinMaxScaler()   0 -> 0.5018532975191449
  417/3780 0.050 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5612507407994983
  418/3780 0.050 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4919911987317445
  419/3780 0.050 31.623 1.0 StandardScaler() 0 -> 0.48555356606842137
  420/3780 0.050 31.623 1.0 MinMaxScaler()   0 -> 0.4936205008083075
  421/3780 0.100 0.032 scale StandardScaler() 0 -> 0.5349297339273827
  422/3780 0.100 0.032 scale MinMaxScaler()   0 -> 0.5262563913937041
  423/3780 0.100 0.032 auto StandardScaler() 0 -> 0.5349297339273827
  424/3780 0.100 0.032 auto MinMaxScaler()   0 -> 0.6660598353757482
  425/3780 0.100 0.032 0.01 StandardScaler() 0 -> 0.6132069878979833
  426/3780 0.100 0.032 0.01 MinMaxScaler()   0 -> 0.7359027327858939
  427/3780 0.100 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5519408021085989
  428/3780 0.100 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7020406755542864
  429/3780 0.100 0.032 0.1 StandardScaler() 0 -> 0.5369369833026919
  430/3780 0.100 0.032 0.1 MinMaxScaler()   0 -> 0.6622126832445979
  431/3780 0.100 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6205685198960572
  432/3780 0.100 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.6025866361443853
  433/3780 0.100 0.032 1.0 StandardScaler() 0 -> 0.7241886498811819
  434/3780 0.100 0.032 1.0 MinMaxScaler()   0 -> 0.5404499541682249
  435/3780 0.100 0.040 scale StandardScaler() 0 -> 0.5233076967639502
  436/3780 0.100 0.040 scale MinMaxScaler()   0 -> 0.5159826427475402
  437/3780 0.100 0.040 auto StandardScaler() 0 -> 0.5233076967639501
  438/3780 0.100 0.040 auto MinMaxScaler()   0 -> 0.6549265330545183
  439/3780 0.100 0.040 0.01 StandardScaler() 0 -> 0.5969632257904878
  440/3780 0.100 0.040 0.01 MinMaxScaler()   0 -> 0.7308228553659039
  441/3780 0.100 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5377404414653157
  442/3780 0.100 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.6941772999272882
  443/3780 0.100 0.040 0.1 StandardScaler() 0 -> 0.5249178755341075
  444/3780 0.100 0.040 0.1 MinMaxScaler()   0 -> 0.6505594664258831
  445/3780 0.100 0.040 0.31622776601683794 StandardScaler() 0 -> 0.6006224352203234
  446/3780 0.100 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5854480638205254
  447/3780 0.100 0.040 1.0 StandardScaler() 0 -> 0.7160844076627182
  448/3780 0.100 0.040 1.0 MinMaxScaler()   0 -> 0.5277577199328699
  449/3780 0.100 0.051 scale StandardScaler() 0 -> 0.5144465104860847
  450/3780 0.100 0.051 scale MinMaxScaler()   0 -> 0.5094695586819235
  451/3780 0.100 0.051 auto StandardScaler() 0 -> 0.5144465104860848
  452/3780 0.100 0.051 auto MinMaxScaler()   0 -> 0.6426872983263455
  453/3780 0.100 0.051 0.01 StandardScaler() 0 -> 0.5816143862256232
  454/3780 0.100 0.051 0.01 MinMaxScaler()   0 -> 0.7246090335414822
  455/3780 0.100 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5261484647638434
  456/3780 0.100 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6869884345471734
  457/3780 0.100 0.051 0.1 StandardScaler() 0 -> 0.5161184040315704
  458/3780 0.100 0.051 0.1 MinMaxScaler()   0 -> 0.6375835531765016
  459/3780 0.100 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5809249876748871
  460/3780 0.100 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5693540331556103
  461/3780 0.100 0.051 1.0 StandardScaler() 0 -> 0.7062166255642518
  462/3780 0.100 0.051 1.0 MinMaxScaler()   0 -> 0.5190149544401476
  463/3780 0.100 0.065 scale StandardScaler() 0 -> 0.5077120101055533
  464/3780 0.100 0.065 scale MinMaxScaler()   0 -> 0.5050212120671711
  465/3780 0.100 0.065 auto StandardScaler() 0 -> 0.5077120101055533
  466/3780 0.100 0.065 auto MinMaxScaler()   0 -> 0.6292773185874002
  467/3780 0.100 0.065 0.01 StandardScaler() 0 -> 0.5674977408187999
  468/3780 0.100 0.065 0.01 MinMaxScaler()   0 -> 0.7170744903147802
  469/3780 0.100 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5180658376332344
  470/3780 0.100 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6782696528858182
  471/3780 0.100 0.065 0.1 StandardScaler() 0 -> 0.5083516505914428
  472/3780 0.100 0.065 0.1 MinMaxScaler()   0 -> 0.6236977220450001
  473/3780 0.100 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5627053210265194
  474/3780 0.100 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5563960464327579
  475/3780 0.100 0.065 1.0 StandardScaler() 0 -> 0.6944380001500292
  476/3780 0.100 0.065 1.0 MinMaxScaler()   0 -> 0.5122878555413549
  477/3780 0.100 0.082 scale StandardScaler() 0 -> 0.5022053774208236
  478/3780 0.100 0.082 scale MinMaxScaler()   0 -> 0.5010579665215464
  479/3780 0.100 0.082 auto StandardScaler() 0 -> 0.5022053774208235
  480/3780 0.100 0.082 auto MinMaxScaler()   0 -> 0.6151096313831687
  481/3780 0.100 0.082 0.01 StandardScaler() 0 -> 0.5568016145551912
  482/3780 0.100 0.082 0.01 MinMaxScaler()   0 -> 0.7085101356578866
  483/3780 0.100 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5121923616333088
  484/3780 0.100 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6688292175747309
  485/3780 0.100 0.082 0.1 StandardScaler() 0 -> 0.5029798677737486
  486/3780 0.100 0.082 0.1 MinMaxScaler()   0 -> 0.6091594061448711
  487/3780 0.100 0.082 0.31622776601683794 StandardScaler() 0 -> 0.546635654726574
  488/3780 0.100 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5452984781777191
  489/3780 0.100 0.082 1.0 StandardScaler() 0 -> 0.6803919863113784
  490/3780 0.100 0.082 1.0 MinMaxScaler()   0 -> 0.5078356748154297
  491/3780 0.100 0.104 scale StandardScaler() 0 -> 0.4978405996291296
  492/3780 0.100 0.104 scale MinMaxScaler()   0 -> 0.49711467075766547
  493/3780 0.100 0.104 auto StandardScaler() 0 -> 0.4978405996291295
  494/3780 0.100 0.104 auto MinMaxScaler()   0 -> 0.5994491738320077
  495/3780 0.100 0.104 0.01 StandardScaler() 0 -> 0.5471884600288838
  496/3780 0.100 0.104 0.01 MinMaxScaler()   0 -> 0.7002818252093396
  497/3780 0.100 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5072293020798618
  498/3780 0.100 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6581926329347013
  499/3780 0.100 0.104 0.1 StandardScaler() 0 -> 0.49840873040957767
  500/3780 0.100 0.104 0.1 MinMaxScaler()   0 -> 0.5931834002202564
  501/3780 0.100 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5329851125025944
  502/3780 0.100 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5376707967517069
  503/3780 0.100 0.104 1.0 StandardScaler() 0 -> 0.6640110978145867
  504/3780 0.100 0.104 1.0 MinMaxScaler()   0 -> 0.5040615845271951
  505/3780 0.100 0.132 scale StandardScaler() 0 -> 0.4943320232198333
  506/3780 0.100 0.132 scale MinMaxScaler()   0 -> 0.4941342742847499
  507/3780 0.100 0.132 auto StandardScaler() 0 -> 0.49433202321983316
  508/3780 0.100 0.132 auto MinMaxScaler()   0 -> 0.5845021165542609
  509/3780 0.100 0.132 0.01 StandardScaler() 0 -> 0.538749308143856
  510/3780 0.100 0.132 0.01 MinMaxScaler()   0 -> 0.6924646796990278
  511/3780 0.100 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5034569090377948
  512/3780 0.100 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6464959519233989
  513/3780 0.100 0.132 0.1 StandardScaler() 0 -> 0.49464696176503736
  514/3780 0.100 0.132 0.1 MinMaxScaler()   0 -> 0.578707405787238
  515/3780 0.100 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5206423987507397
  516/3780 0.100 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5319342851768708
  517/3780 0.100 0.132 1.0 StandardScaler() 0 -> 0.6457739037969696
  518/3780 0.100 0.132 1.0 MinMaxScaler()   0 -> 0.5017042957148247
  519/3780 0.100 0.168 scale StandardScaler() 0 -> 0.4914648170417797
  520/3780 0.100 0.168 scale MinMaxScaler()   0 -> 0.49183486532594506
  521/3780 0.100 0.168 auto StandardScaler() 0 -> 0.4914648170417797
  522/3780 0.100 0.168 auto MinMaxScaler()   0 -> 0.5714835617871309
  523/3780 0.100 0.168 0.01 StandardScaler() 0 -> 0.5322055700790664
  524/3780 0.100 0.168 0.01 MinMaxScaler()   0 -> 0.6852014808263333
  525/3780 0.100 0.168 0.03162277660168379 StandardScaler() 0 -> 0.500592432376298
  526/3780 0.100 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6335137902516008
  527/3780 0.100 0.168 0.1 StandardScaler() 0 -> 0.49100516286477386
  528/3780 0.100 0.168 0.1 MinMaxScaler()   0 -> 0.5668651336709538
  529/3780 0.100 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5094888456564314
  530/3780 0.100 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.526719341403465
  531/3780 0.100 0.168 1.0 StandardScaler() 0 -> 0.6260569999320288
  532/3780 0.100 0.168 1.0 MinMaxScaler()   0 -> 0.49914458142673174
  533/3780 0.100 0.213 scale StandardScaler() 0 -> 0.48834902680099884
  534/3780 0.100 0.213 scale MinMaxScaler()   0 -> 0.4891552983084448
  535/3780 0.100 0.213 auto StandardScaler() 0 -> 0.48834902680099884
  536/3780 0.100 0.213 auto MinMaxScaler()   0 -> 0.5617232554602616
  537/3780 0.100 0.213 0.01 StandardScaler() 0 -> 0.526700197184956
  538/3780 0.100 0.213 0.01 MinMaxScaler()   0 -> 0.6761436161460398
  539/3780 0.100 0.213 0.03162277660168379 StandardScaler() 0 -> 0.49760372334880865
  540/3780 0.100 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6199268911407706
  541/3780 0.100 0.213 0.1 StandardScaler() 0 -> 0.48794263189065473
  542/3780 0.100 0.213 0.1 MinMaxScaler()   0 -> 0.5585708939538419
  543/3780 0.100 0.213 0.31622776601683794 StandardScaler() 0 -> 0.49940216337412274
  544/3780 0.100 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5220010927285198
  545/3780 0.100 0.213 1.0 StandardScaler() 0 -> 0.6050947635060709
  546/3780 0.100 0.213 1.0 MinMaxScaler()   0 -> 0.49713702873348375
  547/3780 0.100 0.270 scale StandardScaler() 0 -> 0.48527957229295615
  548/3780 0.100 0.270 scale MinMaxScaler()   0 -> 0.48692063139607633
  549/3780 0.100 0.270 auto StandardScaler() 0 -> 0.4852795722929561
  550/3780 0.100 0.270 auto MinMaxScaler()   0 -> 0.5551341926043883
  551/3780 0.100 0.270 0.01 StandardScaler() 0 -> 0.5222522658346923
  552/3780 0.100 0.270 0.01 MinMaxScaler()   0 -> 0.6666783106324731
  553/3780 0.100 0.270 0.03162277660168379 StandardScaler() 0 -> 0.49544519548758603
  554/3780 0.100 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.6052223619017029
  555/3780 0.100 0.270 0.1 StandardScaler() 0 -> 0.4846905799780028
  556/3780 0.100 0.270 0.1 MinMaxScaler()   0 -> 0.5525146614124165
  557/3780 0.100 0.270 0.31622776601683794 StandardScaler() 0 -> 0.4902171982926434
  558/3780 0.100 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5181558010610424
  559/3780 0.100 0.270 1.0 StandardScaler() 0 -> 0.583907753497848
  560/3780 0.100 0.270 1.0 MinMaxScaler()   0 -> 0.49559664552386457
  561/3780 0.100 0.342 scale StandardScaler() 0 -> 0.48253316483548486
  562/3780 0.100 0.342 scale MinMaxScaler()   0 -> 0.48520204168433095
  563/3780 0.100 0.342 auto StandardScaler() 0 -> 0.482533164835485
  564/3780 0.100 0.342 auto MinMaxScaler()   0 -> 0.5504087070523677
  565/3780 0.100 0.342 0.01 StandardScaler() 0 -> 0.5180478393146118
  566/3780 0.100 0.342 0.01 MinMaxScaler()   0 -> 0.655782429136944
  567/3780 0.100 0.342 0.03162277660168379 StandardScaler() 0 -> 0.49441496715330185
  568/3780 0.100 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5904606277616443
  569/3780 0.100 0.342 0.1 StandardScaler() 0 -> 0.4817730056758737
  570/3780 0.100 0.342 0.1 MinMaxScaler()   0 -> 0.547857034961284
  571/3780 0.100 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4822026811815756
  572/3780 0.100 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.513984576132152
  573/3780 0.100 0.342 1.0 StandardScaler() 0 -> 0.5631874581603736
  574/3780 0.100 0.342 1.0 MinMaxScaler()   0 -> 0.4942353764049117
  575/3780 0.100 0.434 scale StandardScaler() 0 -> 0.48024172748964716
  576/3780 0.100 0.434 scale MinMaxScaler()   0 -> 0.48318604338037535
  577/3780 0.100 0.434 auto StandardScaler() 0 -> 0.4802417274896474
  578/3780 0.100 0.434 auto MinMaxScaler()   0 -> 0.5465463857757088
  579/3780 0.100 0.434 0.01 StandardScaler() 0 -> 0.5136520545966908
  580/3780 0.100 0.434 0.01 MinMaxScaler()   0 -> 0.6436874163882988
  581/3780 0.100 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49349603277111304
  582/3780 0.100 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5769889164314436
  583/3780 0.100 0.434 0.1 StandardScaler() 0 -> 0.4798316136136331
  584/3780 0.100 0.434 0.1 MinMaxScaler()   0 -> 0.544385686689866
  585/3780 0.100 0.434 0.31622776601683794 StandardScaler() 0 -> 0.47598196698358936
  586/3780 0.100 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5103168048835748
  587/3780 0.100 0.434 1.0 StandardScaler() 0 -> 0.543199736836456
  588/3780 0.100 0.434 1.0 MinMaxScaler()   0 -> 0.4933212131545981
  589/3780 0.100 0.551 scale StandardScaler() 0 -> 0.47871878652773886
  590/3780 0.100 0.551 scale MinMaxScaler()   0 -> 0.4811501562413611
  591/3780 0.100 0.551 auto StandardScaler() 0 -> 0.4787187865277389
  592/3780 0.100 0.551 auto MinMaxScaler()   0 -> 0.5436914732906393
  593/3780 0.100 0.551 0.01 StandardScaler() 0 -> 0.5098179011572286
  594/3780 0.100 0.551 0.01 MinMaxScaler()   0 -> 0.6306941009102683
  595/3780 0.100 0.551 0.03162277660168379 StandardScaler() 0 -> 0.49191384815910116
  596/3780 0.100 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5670950149143068
  597/3780 0.100 0.551 0.1 StandardScaler() 0 -> 0.4783273084450048
  598/3780 0.100 0.551 0.1 MinMaxScaler()   0 -> 0.5412776159452145
  599/3780 0.100 0.551 0.31622776601683794 StandardScaler() 0 -> 0.47112597644158877
  600/3780 0.100 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5067348303349396
  601/3780 0.100 0.551 1.0 StandardScaler() 0 -> 0.52516308299533
  602/3780 0.100 0.551 1.0 MinMaxScaler()   0 -> 0.4922295167971315
  603/3780 0.100 0.700 scale StandardScaler() 0 -> 0.4776316419731807
  604/3780 0.100 0.700 scale MinMaxScaler()   0 -> 0.4793893706410925
  605/3780 0.100 0.700 auto StandardScaler() 0 -> 0.47763164197318075
  606/3780 0.100 0.700 auto MinMaxScaler()   0 -> 0.5400765101514361
  607/3780 0.100 0.700 0.01 StandardScaler() 0 -> 0.5064291620983427
  608/3780 0.100 0.700 0.01 MinMaxScaler()   0 -> 0.6171881465744146
  609/3780 0.100 0.700 0.03162277660168379 StandardScaler() 0 -> 0.49025833797675955
  610/3780 0.100 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5604085806164975
  611/3780 0.100 0.700 0.1 StandardScaler() 0 -> 0.4768459071837609
  612/3780 0.100 0.700 0.1 MinMaxScaler()   0 -> 0.5377086612484944
  613/3780 0.100 0.700 0.31622776601683794 StandardScaler() 0 -> 0.46806763221647474
  614/3780 0.100 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5046029101680065
  615/3780 0.100 0.700 1.0 StandardScaler() 0 -> 0.509587688063752
  616/3780 0.100 0.700 1.0 MinMaxScaler()   0 -> 0.49022234896485584
  617/3780 0.100 0.888 scale StandardScaler() 0 -> 0.4766635921162978
  618/3780 0.100 0.888 scale MinMaxScaler()   0 -> 0.4779284171846258
  619/3780 0.100 0.888 auto StandardScaler() 0 -> 0.47666359211629716
  620/3780 0.100 0.888 auto MinMaxScaler()   0 -> 0.5366281259604778
  621/3780 0.100 0.888 0.01 StandardScaler() 0 -> 0.5042764059177678
  622/3780 0.100 0.888 0.01 MinMaxScaler()   0 -> 0.6022538954111104
  623/3780 0.100 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4884560506597638
  624/3780 0.100 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5567707660548521
  625/3780 0.100 0.888 0.1 StandardScaler() 0 -> 0.47533318389259466
  626/3780 0.100 0.888 0.1 MinMaxScaler()   0 -> 0.5339294435448553
  627/3780 0.100 0.888 0.31622776601683794 StandardScaler() 0 -> 0.46684387796223986
  628/3780 0.100 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5026373053223053
  629/3780 0.100 0.888 1.0 StandardScaler() 0 -> 0.4972504647634007
  630/3780 0.100 0.888 1.0 MinMaxScaler()   0 -> 0.4883036265273635
  631/3780 0.100 1.126 scale StandardScaler() 0 -> 0.4753341310761581
  632/3780 0.100 1.126 scale MinMaxScaler()   0 -> 0.47682662822581395
  633/3780 0.100 1.126 auto StandardScaler() 0 -> 0.4753341310761585
  634/3780 0.100 1.126 auto MinMaxScaler()   0 -> 0.5329387375252127
  635/3780 0.100 1.126 0.01 StandardScaler() 0 -> 0.5022739058077571
  636/3780 0.100 1.126 0.01 MinMaxScaler()   0 -> 0.5878722443102125
  637/3780 0.100 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48742352021058544
  638/3780 0.100 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5540878782650774
  639/3780 0.100 1.126 0.1 StandardScaler() 0 -> 0.47424179822315987
  640/3780 0.100 1.126 0.1 MinMaxScaler()   0 -> 0.530142287513462
  641/3780 0.100 1.126 0.31622776601683794 StandardScaler() 0 -> 0.46682448264015813
  642/3780 0.100 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5011237421171298
  643/3780 0.100 1.126 1.0 StandardScaler() 0 -> 0.4909845304340323
  644/3780 0.100 1.126 1.0 MinMaxScaler()   0 -> 0.48692022049607914
  645/3780 0.100 1.429 scale StandardScaler() 0 -> 0.474140838818006
  646/3780 0.100 1.429 scale MinMaxScaler()   0 -> 0.476455376780053
  647/3780 0.100 1.429 auto StandardScaler() 0 -> 0.4741408388180057
  648/3780 0.100 1.429 auto MinMaxScaler()   0 -> 0.5293983160929647
  649/3780 0.100 1.429 0.01 StandardScaler() 0 -> 0.5013891730794916
  650/3780 0.100 1.429 0.01 MinMaxScaler()   0 -> 0.5756743560809251
  651/3780 0.100 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48655880121022516
  652/3780 0.100 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.551706012164311
  653/3780 0.100 1.429 0.1 StandardScaler() 0 -> 0.47320797799152053
  654/3780 0.100 1.429 0.1 MinMaxScaler()   0 -> 0.5266215701015984
  655/3780 0.100 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4686404746574397
  656/3780 0.100 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.500042505577304
  657/3780 0.100 1.429 1.0 StandardScaler() 0 -> 0.4876998912264772
  658/3780 0.100 1.429 1.0 MinMaxScaler()   0 -> 0.48576336219884547
  659/3780 0.100 1.814 scale StandardScaler() 0 -> 0.4735715102143718
  660/3780 0.100 1.814 scale MinMaxScaler()   0 -> 0.4760446022151886
  661/3780 0.100 1.814 auto StandardScaler() 0 -> 0.4735715102143714
  662/3780 0.100 1.814 auto MinMaxScaler()   0 -> 0.5257758856999645
  663/3780 0.100 1.814 0.01 StandardScaler() 0 -> 0.5004094065374772
  664/3780 0.100 1.814 0.01 MinMaxScaler()   0 -> 0.5665720180217542
  665/3780 0.100 1.814 0.03162277660168379 StandardScaler() 0 -> 0.48586145647933004
  666/3780 0.100 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.550056321913005
  667/3780 0.100 1.814 0.1 StandardScaler() 0 -> 0.47358875587706256
  668/3780 0.100 1.814 0.1 MinMaxScaler()   0 -> 0.5229067969353434
  669/3780 0.100 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4714890929383917
  670/3780 0.100 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.49880158026465543
  671/3780 0.100 1.814 1.0 StandardScaler() 0 -> 0.4860428184849399
  672/3780 0.100 1.814 1.0 MinMaxScaler()   0 -> 0.4849527238050539
  673/3780 0.100 2.302 scale StandardScaler() 0 -> 0.4740991497145077
  674/3780 0.100 2.302 scale MinMaxScaler()   0 -> 0.4754414134511107
  675/3780 0.100 2.302 auto StandardScaler() 0 -> 0.47409914971450884
  676/3780 0.100 2.302 auto MinMaxScaler()   0 -> 0.5218489696413288
  677/3780 0.100 2.302 0.01 StandardScaler() 0 -> 0.4993131619602491
  678/3780 0.100 2.302 0.01 MinMaxScaler()   0 -> 0.5615232927574111
  679/3780 0.100 2.302 0.03162277660168379 StandardScaler() 0 -> 0.4845131745613147
  680/3780 0.100 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5479180805465952
  681/3780 0.100 2.302 0.1 StandardScaler() 0 -> 0.47374654036595515
  682/3780 0.100 2.302 0.1 MinMaxScaler()   0 -> 0.5189625414989957
  683/3780 0.100 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4749156615517629
  684/3780 0.100 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.4984867318898621
  685/3780 0.100 2.302 1.0 StandardScaler() 0 -> 0.4860640980027749
  686/3780 0.100 2.302 1.0 MinMaxScaler()   0 -> 0.48386679369041347
  687/3780 0.100 2.921 scale StandardScaler() 0 -> 0.47478960675602533
  688/3780 0.100 2.921 scale MinMaxScaler()   0 -> 0.4751050965130265
  689/3780 0.100 2.921 auto StandardScaler() 0 -> 0.4747896067560255
  690/3780 0.100 2.921 auto MinMaxScaler()   0 -> 0.5179070444208849
  691/3780 0.100 2.921 0.01 StandardScaler() 0 -> 0.498941829681048
  692/3780 0.100 2.921 0.01 MinMaxScaler()   0 -> 0.5584837982340841
  693/3780 0.100 2.921 0.03162277660168379 StandardScaler() 0 -> 0.4834535371740951
  694/3780 0.100 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5458997716163552
  695/3780 0.100 2.921 0.1 StandardScaler() 0 -> 0.4754859015629605
  696/3780 0.100 2.921 0.1 MinMaxScaler()   0 -> 0.5150612486980345
  697/3780 0.100 2.921 0.31622776601683794 StandardScaler() 0 -> 0.47991533111721013
  698/3780 0.100 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.497949827486277
  699/3780 0.100 2.921 1.0 StandardScaler() 0 -> 0.48585123951999987
  700/3780 0.100 2.921 1.0 MinMaxScaler()   0 -> 0.4829816144167414
  701/3780 0.100 3.707 scale StandardScaler() 0 -> 0.4774081482432833
  702/3780 0.100 3.707 scale MinMaxScaler()   0 -> 0.47518268257138546
  703/3780 0.100 3.707 auto StandardScaler() 0 -> 0.4774081482432833
  704/3780 0.100 3.707 auto MinMaxScaler()   0 -> 0.5141707376495898
  705/3780 0.100 3.707 0.01 StandardScaler() 0 -> 0.4991888081124061
  706/3780 0.100 3.707 0.01 MinMaxScaler()   0 -> 0.5563655235660211
  707/3780 0.100 3.707 0.03162277660168379 StandardScaler() 0 -> 0.48238871734250277
  708/3780 0.100 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5435973061381422
  709/3780 0.100 3.707 0.1 StandardScaler() 0 -> 0.4785370607212862
  710/3780 0.100 3.707 0.1 MinMaxScaler()   0 -> 0.5116373264436402
  711/3780 0.100 3.707 0.31622776601683794 StandardScaler() 0 -> 0.48694333422397335
  712/3780 0.100 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4969930011008896
  713/3780 0.100 3.707 1.0 StandardScaler() 0 -> 0.48587013400294693
  714/3780 0.100 3.707 1.0 MinMaxScaler()   0 -> 0.48215416417398577
  715/3780 0.100 4.703 scale StandardScaler() 0 -> 0.48113393042519875
  716/3780 0.100 4.703 scale MinMaxScaler()   0 -> 0.47750152112006705
  717/3780 0.100 4.703 auto StandardScaler() 0 -> 0.48113393042520053
  718/3780 0.100 4.703 auto MinMaxScaler()   0 -> 0.5112443379174744
  719/3780 0.100 4.703 0.01 StandardScaler() 0 -> 0.4987222391378278
  720/3780 0.100 4.703 0.01 MinMaxScaler()   0 -> 0.5547282881840082
  721/3780 0.100 4.703 0.03162277660168379 StandardScaler() 0 -> 0.4814794029962434
  722/3780 0.100 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5409208513823048
  723/3780 0.100 4.703 0.1 StandardScaler() 0 -> 0.4816630589457039
  724/3780 0.100 4.703 0.1 MinMaxScaler()   0 -> 0.5086920388525754
  725/3780 0.100 4.703 0.31622776601683794 StandardScaler() 0 -> 0.4929105066865396
  726/3780 0.100 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.49623293045575667
  727/3780 0.100 4.703 1.0 StandardScaler() 0 -> 0.48599601034359946
  728/3780 0.100 4.703 1.0 MinMaxScaler()   0 -> 0.4821028452363245
  729/3780 0.100 5.968 scale StandardScaler() 0 -> 0.48441355133324243
  730/3780 0.100 5.968 scale MinMaxScaler()   0 -> 0.4813560346701187
  731/3780 0.100 5.968 auto StandardScaler() 0 -> 0.48441355133324443
  732/3780 0.100 5.968 auto MinMaxScaler()   0 -> 0.5082070577727078
  733/3780 0.100 5.968 0.01 StandardScaler() 0 -> 0.49767995213872435
  734/3780 0.100 5.968 0.01 MinMaxScaler()   0 -> 0.5537437816473146
  735/3780 0.100 5.968 0.03162277660168379 StandardScaler() 0 -> 0.4811087255393076
  736/3780 0.100 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5384740953515833
  737/3780 0.100 5.968 0.1 StandardScaler() 0 -> 0.48758736714941514
  738/3780 0.100 5.968 0.1 MinMaxScaler()   0 -> 0.506462686487268
  739/3780 0.100 5.968 0.31622776601683794 StandardScaler() 0 -> 0.4996965724099797
  740/3780 0.100 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.4954402411450727
  741/3780 0.100 5.968 1.0 StandardScaler() 0 -> 0.48618975945820503
  742/3780 0.100 5.968 1.0 MinMaxScaler()   0 -> 0.4820360878239665
  743/3780 0.100 7.574 scale StandardScaler() 0 -> 0.49032932166254
  744/3780 0.100 7.574 scale MinMaxScaler()   0 -> 0.4856438567814352
  745/3780 0.100 7.574 auto StandardScaler() 0 -> 0.490329321662537
  746/3780 0.100 7.574 auto MinMaxScaler()   0 -> 0.5060879564716095
  747/3780 0.100 7.574 0.01 StandardScaler() 0 -> 0.4967569778323988
  748/3780 0.100 7.574 0.01 MinMaxScaler()   0 -> 0.552908146658191
  749/3780 0.100 7.574 0.03162277660168379 StandardScaler() 0 -> 0.4811215039469477
  750/3780 0.100 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5352727731278936
  751/3780 0.100 7.574 0.1 StandardScaler() 0 -> 0.4945246566248529
  752/3780 0.100 7.574 0.1 MinMaxScaler()   0 -> 0.5046583390718432
  753/3780 0.100 7.574 0.31622776601683794 StandardScaler() 0 -> 0.5090664776655826
  754/3780 0.100 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.4948929842417297
  755/3780 0.100 7.574 1.0 StandardScaler() 0 -> 0.486382063915811
  756/3780 0.100 7.574 1.0 MinMaxScaler()   0 -> 0.48202949715013793
  757/3780 0.100 9.611 scale StandardScaler() 0 -> 0.4976141014678224
  758/3780 0.100 9.611 scale MinMaxScaler()   0 -> 0.4893741618526464
  759/3780 0.100 9.611 auto StandardScaler() 0 -> 0.49761410146782276
  760/3780 0.100 9.611 auto MinMaxScaler()   0 -> 0.5044074422153472
  761/3780 0.100 9.611 0.01 StandardScaler() 0 -> 0.4958807798773368
  762/3780 0.100 9.611 0.01 MinMaxScaler()   0 -> 0.5517139627668771
  763/3780 0.100 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4814395251716823
  764/3780 0.100 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5317785912262327
  765/3780 0.100 9.611 0.1 StandardScaler() 0 -> 0.5030871740150807
  766/3780 0.100 9.611 0.1 MinMaxScaler()   0 -> 0.5031791514910157
  767/3780 0.100 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5186372536254732
  768/3780 0.100 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4939659442707207
  769/3780 0.100 9.611 1.0 StandardScaler() 0 -> 0.48639348390068604
  770/3780 0.100 9.611 1.0 MinMaxScaler()   0 -> 0.4827016185555057
  771/3780 0.100 12.196 scale StandardScaler() 0 -> 0.507094241506994
  772/3780 0.100 12.196 scale MinMaxScaler()   0 -> 0.4934777209461369
  773/3780 0.100 12.196 auto StandardScaler() 0 -> 0.507094241506997
  774/3780 0.100 12.196 auto MinMaxScaler()   0 -> 0.502780248117338
  775/3780 0.100 12.196 0.01 StandardScaler() 0 -> 0.4952054346317505
  776/3780 0.100 12.196 0.01 MinMaxScaler()   0 -> 0.5507495987446004
  777/3780 0.100 12.196 0.03162277660168379 StandardScaler() 0 -> 0.48251123723099915
  778/3780 0.100 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5279353337767563
  779/3780 0.100 12.196 0.1 StandardScaler() 0 -> 0.5148181565096288
  780/3780 0.100 12.196 0.1 MinMaxScaler()   0 -> 0.5020147127129305
  781/3780 0.100 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5269212876897509
  782/3780 0.100 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.49326527962177646
  783/3780 0.100 12.196 1.0 StandardScaler() 0 -> 0.48640667773675617
  784/3780 0.100 12.196 1.0 MinMaxScaler()   0 -> 0.4835296716274608
  785/3780 0.100 15.476 scale StandardScaler() 0 -> 0.5191504846445183
  786/3780 0.100 15.476 scale MinMaxScaler()   0 -> 0.4988715665057384
  787/3780 0.100 15.476 auto StandardScaler() 0 -> 0.5191504846445115
  788/3780 0.100 15.476 auto MinMaxScaler()   0 -> 0.501879482891696
  789/3780 0.100 15.476 0.01 StandardScaler() 0 -> 0.49450868688708605
  790/3780 0.100 15.476 0.01 MinMaxScaler()   0 -> 0.5496835857481418
  791/3780 0.100 15.476 0.03162277660168379 StandardScaler() 0 -> 0.4829453725395188
  792/3780 0.100 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5245679606055834
  793/3780 0.100 15.476 0.1 StandardScaler() 0 -> 0.5304382227984008
  794/3780 0.100 15.476 0.1 MinMaxScaler()   0 -> 0.501227369010865
  795/3780 0.100 15.476 0.31622776601683794 StandardScaler() 0 -> 0.532191145403773
  796/3780 0.100 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.49336238608284605
  797/3780 0.100 15.476 1.0 StandardScaler() 0 -> 0.48640849582906814
  798/3780 0.100 15.476 1.0 MinMaxScaler()   0 -> 0.48402412588192467
  799/3780 0.100 19.638 scale StandardScaler() 0 -> 0.5355234362615252
  800/3780 0.100 19.638 scale MinMaxScaler()   0 -> 0.5081661672837512
  801/3780 0.100 19.638 auto StandardScaler() 0 -> 0.5355234362615223
  802/3780 0.100 19.638 auto MinMaxScaler()   0 -> 0.5013483654159947
  803/3780 0.100 19.638 0.01 StandardScaler() 0 -> 0.49337362950196056
  804/3780 0.100 19.638 0.01 MinMaxScaler()   0 -> 0.5484108477674613
  805/3780 0.100 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4842699810612829
  806/3780 0.100 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5209833155670308
  807/3780 0.100 19.638 0.1 StandardScaler() 0 -> 0.5482628416103601
  808/3780 0.100 19.638 0.1 MinMaxScaler()   0 -> 0.5014754467211406
  809/3780 0.100 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5366379205210722
  810/3780 0.100 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.4930358911179343
  811/3780 0.100 19.638 1.0 StandardScaler() 0 -> 0.48640849582906814
  812/3780 0.100 19.638 1.0 MinMaxScaler()   0 -> 0.4851013816243847
  813/3780 0.100 24.920 scale StandardScaler() 0 -> 0.5537228722153048
  814/3780 0.100 24.920 scale MinMaxScaler()   0 -> 0.5194257383448208
  815/3780 0.100 24.920 auto StandardScaler() 0 -> 0.5537228722153085
  816/3780 0.100 24.920 auto MinMaxScaler()   0 -> 0.5014693229433919
  817/3780 0.100 24.920 0.01 StandardScaler() 0 -> 0.4924018531356644
  818/3780 0.100 24.920 0.01 MinMaxScaler()   0 -> 0.5466204853453185
  819/3780 0.100 24.920 0.03162277660168379 StandardScaler() 0 -> 0.48768829010304265
  820/3780 0.100 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5172824012904434
  821/3780 0.100 24.920 0.1 StandardScaler() 0 -> 0.5698509777587301
  822/3780 0.100 24.920 0.1 MinMaxScaler()   0 -> 0.5004267625760654
  823/3780 0.100 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5410570633122722
  824/3780 0.100 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.4919641555338174
  825/3780 0.100 24.920 1.0 StandardScaler() 0 -> 0.48640849582906814
  826/3780 0.100 24.920 1.0 MinMaxScaler()   0 -> 0.4870447082334668
  827/3780 0.100 31.623 scale StandardScaler() 0 -> 0.5769057691215922
  828/3780 0.100 31.623 scale MinMaxScaler()   0 -> 0.5338176174757588
  829/3780 0.100 31.623 auto StandardScaler() 0 -> 0.576905769121595
  830/3780 0.100 31.623 auto MinMaxScaler()   0 -> 0.5004178235020801
  831/3780 0.100 31.623 0.01 StandardScaler() 0 -> 0.49234051587592503
  832/3780 0.100 31.623 0.01 MinMaxScaler()   0 -> 0.5444945733520847
  833/3780 0.100 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4904274246947377
  834/3780 0.100 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.51367069211362
  835/3780 0.100 31.623 0.1 StandardScaler() 0 -> 0.5944468382847211
  836/3780 0.100 31.623 0.1 MinMaxScaler()   0 -> 0.5000592886978467
  837/3780 0.100 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5438794120479101
  838/3780 0.100 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.49075124733940284
  839/3780 0.100 31.623 1.0 StandardScaler() 0 -> 0.48640849582906814
  840/3780 0.100 31.623 1.0 MinMaxScaler()   0 -> 0.4902619518613041
  841/3780 0.150 0.032 scale StandardScaler() 0 -> 0.53082299269767
  842/3780 0.150 0.032 scale MinMaxScaler()   0 -> 0.5229781187063733
  843/3780 0.150 0.032 auto StandardScaler() 0 -> 0.5308229926976699
  844/3780 0.150 0.032 auto MinMaxScaler()   0 -> 0.6504138507006866
  845/3780 0.150 0.032 0.01 StandardScaler() 0 -> 0.5981653136246633
  846/3780 0.150 0.032 0.01 MinMaxScaler()   0 -> 0.7376539372877273
  847/3780 0.150 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5449296642356711
  848/3780 0.150 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.6977569042045325
  849/3780 0.150 0.032 0.1 StandardScaler() 0 -> 0.5326126913540069
  850/3780 0.150 0.032 0.1 MinMaxScaler()   0 -> 0.6458954620942747
  851/3780 0.150 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6106945522240904
  852/3780 0.150 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5866708480437643
  853/3780 0.150 0.032 1.0 StandardScaler() 0 -> 0.728257548010982
  854/3780 0.150 0.032 1.0 MinMaxScaler()   0 -> 0.5346752115132645
  855/3780 0.150 0.040 scale StandardScaler() 0 -> 0.5204251815386379
  856/3780 0.150 0.040 scale MinMaxScaler()   0 -> 0.5150684308437418
  857/3780 0.150 0.040 auto StandardScaler() 0 -> 0.5204251815386379
  858/3780 0.150 0.040 auto MinMaxScaler()   0 -> 0.6384872560996077
  859/3780 0.150 0.040 0.01 StandardScaler() 0 -> 0.5831388000206928
  860/3780 0.150 0.040 0.01 MinMaxScaler()   0 -> 0.7320256625129898
  861/3780 0.150 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5327582561332195
  862/3780 0.150 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.6852516296689038
  863/3780 0.150 0.040 0.1 StandardScaler() 0 -> 0.5220434839836581
  864/3780 0.150 0.040 0.1 MinMaxScaler()   0 -> 0.6340820605259161
  865/3780 0.150 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5922695978105973
  866/3780 0.150 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5723463333487945
  867/3780 0.150 0.040 1.0 StandardScaler() 0 -> 0.7202257999492646
  868/3780 0.150 0.040 1.0 MinMaxScaler()   0 -> 0.5248049487947042
  869/3780 0.150 0.051 scale StandardScaler() 0 -> 0.5127795631944375
  870/3780 0.150 0.051 scale MinMaxScaler()   0 -> 0.5093033675485857
  871/3780 0.150 0.051 auto StandardScaler() 0 -> 0.5127795631944374
  872/3780 0.150 0.051 auto MinMaxScaler()   0 -> 0.6260736169644816
  873/3780 0.150 0.051 0.01 StandardScaler() 0 -> 0.5709741855354418
  874/3780 0.150 0.051 0.01 MinMaxScaler()   0 -> 0.7251159458311548
  875/3780 0.150 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5241611202736048
  876/3780 0.150 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6738658825489194
  877/3780 0.150 0.051 0.1 StandardScaler() 0 -> 0.5138655264534542
  878/3780 0.150 0.051 0.1 MinMaxScaler()   0 -> 0.6211829408357238
  879/3780 0.150 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5735817686387931
  880/3780 0.150 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5600695166972165
  881/3780 0.150 0.051 1.0 StandardScaler() 0 -> 0.710345680259799
  882/3780 0.150 0.051 1.0 MinMaxScaler()   0 -> 0.518256106018962
  883/3780 0.150 0.065 scale StandardScaler() 0 -> 0.50674868035008
  884/3780 0.150 0.065 scale MinMaxScaler()   0 -> 0.5045532274916998
  885/3780 0.150 0.065 auto StandardScaler() 0 -> 0.50674868035008
  886/3780 0.150 0.065 auto MinMaxScaler()   0 -> 0.612489435610696
  887/3780 0.150 0.065 0.01 StandardScaler() 0 -> 0.5610521008234514
  888/3780 0.150 0.065 0.01 MinMaxScaler()   0 -> 0.716703349061734
  889/3780 0.150 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5170361383755951
  890/3780 0.150 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6632488334190637
  891/3780 0.150 0.065 0.1 StandardScaler() 0 -> 0.5076125970564213
  892/3780 0.150 0.065 0.1 MinMaxScaler()   0 -> 0.6072357589495293
  893/3780 0.150 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5566420582157264
  894/3780 0.150 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.550512384093604
  895/3780 0.150 0.065 1.0 StandardScaler() 0 -> 0.6983342106251825
  896/3780 0.150 0.065 1.0 MinMaxScaler()   0 -> 0.5133479339857577
  897/3780 0.150 0.082 scale StandardScaler() 0 -> 0.5012307612083019
  898/3780 0.150 0.082 scale MinMaxScaler()   0 -> 0.5002157483011253
  899/3780 0.150 0.082 auto StandardScaler() 0 -> 0.5012307612083019
  900/3780 0.150 0.082 auto MinMaxScaler()   0 -> 0.5982571220354198
  901/3780 0.150 0.082 0.01 StandardScaler() 0 -> 0.551789057593292
  902/3780 0.150 0.082 0.01 MinMaxScaler()   0 -> 0.7066512598257102
  903/3780 0.150 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5118331830614492
  904/3780 0.150 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6535764067582349
  905/3780 0.150 0.082 0.1 StandardScaler() 0 -> 0.5019195144848271
  906/3780 0.150 0.082 0.1 MinMaxScaler()   0 -> 0.5926559732789568
  907/3780 0.150 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5428057769334469
  908/3780 0.150 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5429392461789486
  909/3780 0.150 0.082 1.0 StandardScaler() 0 -> 0.6841063217763811
  910/3780 0.150 0.082 1.0 MinMaxScaler()   0 -> 0.5086435998995427
  911/3780 0.150 0.104 scale StandardScaler() 0 -> 0.4970382697102093
  912/3780 0.150 0.104 scale MinMaxScaler()   0 -> 0.49626944022076686
  913/3780 0.150 0.104 auto StandardScaler() 0 -> 0.4970382697102092
  914/3780 0.150 0.104 auto MinMaxScaler()   0 -> 0.5848036454143498
  915/3780 0.150 0.104 0.01 StandardScaler() 0 -> 0.5435337006202062
  916/3780 0.150 0.104 0.01 MinMaxScaler()   0 -> 0.6949578397508206
  917/3780 0.150 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5069843745656165
  918/3780 0.150 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6417588329710736
  919/3780 0.150 0.104 0.1 StandardScaler() 0 -> 0.49729145782942147
  920/3780 0.150 0.104 0.1 MinMaxScaler()   0 -> 0.5805686537299725
  921/3780 0.150 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5301902393890039
  922/3780 0.150 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5362820411301761
  923/3780 0.150 0.104 1.0 StandardScaler() 0 -> 0.6678637314227389
  924/3780 0.150 0.104 1.0 MinMaxScaler()   0 -> 0.5046983277437187
  925/3780 0.150 0.132 scale StandardScaler() 0 -> 0.49318111335702747
  926/3780 0.150 0.132 scale MinMaxScaler()   0 -> 0.49318901977765844
  927/3780 0.150 0.132 auto StandardScaler() 0 -> 0.4931811133570274
  928/3780 0.150 0.132 auto MinMaxScaler()   0 -> 0.5737664137709668
  929/3780 0.150 0.132 0.01 StandardScaler() 0 -> 0.5376404043142471
  930/3780 0.150 0.132 0.01 MinMaxScaler()   0 -> 0.68253848888715
  931/3780 0.150 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5029157037310973
  932/3780 0.150 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6298301472744351
  933/3780 0.150 0.132 0.1 StandardScaler() 0 -> 0.49341422120031303
  934/3780 0.150 0.132 0.1 MinMaxScaler()   0 -> 0.5697836444883179
  935/3780 0.150 0.132 0.31622776601683794 StandardScaler() 0 -> 0.518179339960818
  936/3780 0.150 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5313748715096976
  937/3780 0.150 0.132 1.0 StandardScaler() 0 -> 0.6494878241188274
  938/3780 0.150 0.132 1.0 MinMaxScaler()   0 -> 0.5015997747668312
  939/3780 0.150 0.168 scale StandardScaler() 0 -> 0.4901200248556632
  940/3780 0.150 0.168 scale MinMaxScaler()   0 -> 0.4900034055824592
  941/3780 0.150 0.168 auto StandardScaler() 0 -> 0.49012002485566314
  942/3780 0.150 0.168 auto MinMaxScaler()   0 -> 0.5649697968213517
  943/3780 0.150 0.168 0.01 StandardScaler() 0 -> 0.5311519307569617
  944/3780 0.150 0.168 0.01 MinMaxScaler()   0 -> 0.6714322285874764
  945/3780 0.150 0.168 0.03162277660168379 StandardScaler() 0 -> 0.49991595621033835
  946/3780 0.150 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.6169195404222786
  947/3780 0.150 0.168 0.1 StandardScaler() 0 -> 0.4896732030205985
  948/3780 0.150 0.168 0.1 MinMaxScaler()   0 -> 0.5614346413440069
  949/3780 0.150 0.168 0.31622776601683794 StandardScaler() 0 -> 0.506915218168367
  950/3780 0.150 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5264276159422502
  951/3780 0.150 0.168 1.0 StandardScaler() 0 -> 0.6295151766422915
  952/3780 0.150 0.168 1.0 MinMaxScaler()   0 -> 0.4987938816614557
  953/3780 0.150 0.213 scale StandardScaler() 0 -> 0.48647249398612286
  954/3780 0.150 0.213 scale MinMaxScaler()   0 -> 0.4876272544906244
  955/3780 0.150 0.213 auto StandardScaler() 0 -> 0.48647249398612297
  956/3780 0.150 0.213 auto MinMaxScaler()   0 -> 0.5579598671105571
  957/3780 0.150 0.213 0.01 StandardScaler() 0 -> 0.5261066342116101
  958/3780 0.150 0.213 0.01 MinMaxScaler()   0 -> 0.6611032828888032
  959/3780 0.150 0.213 0.03162277660168379 StandardScaler() 0 -> 0.497106362922074
  960/3780 0.150 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.6029603361271308
  961/3780 0.150 0.213 0.1 StandardScaler() 0 -> 0.48594246875798713
  962/3780 0.150 0.213 0.1 MinMaxScaler()   0 -> 0.5554671530520615
  963/3780 0.150 0.213 0.31622776601683794 StandardScaler() 0 -> 0.49737765295231934
  964/3780 0.150 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5222958148536215
  965/3780 0.150 0.213 1.0 StandardScaler() 0 -> 0.6085754909978162
  966/3780 0.150 0.213 1.0 MinMaxScaler()   0 -> 0.496719212346216
  967/3780 0.150 0.270 scale StandardScaler() 0 -> 0.4832069144128952
  968/3780 0.150 0.270 scale MinMaxScaler()   0 -> 0.485450996806515
  969/3780 0.150 0.270 auto StandardScaler() 0 -> 0.48320691441289515
  970/3780 0.150 0.270 auto MinMaxScaler()   0 -> 0.5531650582518511
  971/3780 0.150 0.270 0.01 StandardScaler() 0 -> 0.5220617498803929
  972/3780 0.150 0.270 0.01 MinMaxScaler()   0 -> 0.6509539605766954
  973/3780 0.150 0.270 0.03162277660168379 StandardScaler() 0 -> 0.49517995316512414
  974/3780 0.150 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5897320886282463
  975/3780 0.150 0.270 0.1 StandardScaler() 0 -> 0.4825377653004561
  976/3780 0.150 0.270 0.1 MinMaxScaler()   0 -> 0.5506237402116718
  977/3780 0.150 0.270 0.31622776601683794 StandardScaler() 0 -> 0.48896241781055166
  978/3780 0.150 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5179493827471463
  979/3780 0.150 0.270 1.0 StandardScaler() 0 -> 0.5877011741018526
  980/3780 0.150 0.270 1.0 MinMaxScaler()   0 -> 0.49479234359274255
  981/3780 0.150 0.342 scale StandardScaler() 0 -> 0.4808912077185837
  982/3780 0.150 0.342 scale MinMaxScaler()   0 -> 0.48357929358250523
  983/3780 0.150 0.342 auto StandardScaler() 0 -> 0.48089120771858385
  984/3780 0.150 0.342 auto MinMaxScaler()   0 -> 0.5490648490551552
  985/3780 0.150 0.342 0.01 StandardScaler() 0 -> 0.5175733436251949
  986/3780 0.150 0.342 0.01 MinMaxScaler()   0 -> 0.6395738393071916
  987/3780 0.150 0.342 0.03162277660168379 StandardScaler() 0 -> 0.4935923250729868
  988/3780 0.150 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5789909151214362
  989/3780 0.150 0.342 0.1 StandardScaler() 0 -> 0.48023567509902615
  990/3780 0.150 0.342 0.1 MinMaxScaler()   0 -> 0.5468573241539599
  991/3780 0.150 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4812751024137341
  992/3780 0.150 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5142558925926639
  993/3780 0.150 0.342 1.0 StandardScaler() 0 -> 0.5667816516761629
  994/3780 0.150 0.342 1.0 MinMaxScaler()   0 -> 0.49352736329719943
  995/3780 0.150 0.434 scale StandardScaler() 0 -> 0.47873382950568805
  996/3780 0.150 0.434 scale MinMaxScaler()   0 -> 0.48169772250393955
  997/3780 0.150 0.434 auto StandardScaler() 0 -> 0.4787338295056882
  998/3780 0.150 0.434 auto MinMaxScaler()   0 -> 0.5455468605849126
  999/3780 0.150 0.434 0.01 StandardScaler() 0 -> 0.513622691997934
 1000/3780 0.150 0.434 0.01 MinMaxScaler()   0 -> 0.6269091602815983
 1001/3780 0.150 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49253126103378286
 1002/3780 0.150 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5696392288378148
 1003/3780 0.150 0.434 0.1 StandardScaler() 0 -> 0.47814567229818516
 1004/3780 0.150 0.434 0.1 MinMaxScaler()   0 -> 0.543073880397439
 1005/3780 0.150 0.434 0.31622776601683794 StandardScaler() 0 -> 0.47504683848948676
 1006/3780 0.150 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5109382928575354
 1007/3780 0.150 0.434 1.0 StandardScaler() 0 -> 0.5468148185845181
 1008/3780 0.150 0.434 1.0 MinMaxScaler()   0 -> 0.49264897613588426
 1009/3780 0.150 0.551 scale StandardScaler() 0 -> 0.4769270408811866
 1010/3780 0.150 0.551 scale MinMaxScaler()   0 -> 0.4804607175525372
 1011/3780 0.150 0.551 auto StandardScaler() 0 -> 0.4769270408811867
 1012/3780 0.150 0.551 auto MinMaxScaler()   0 -> 0.5423587387250485
 1013/3780 0.150 0.551 0.01 StandardScaler() 0 -> 0.5094084504375601
 1014/3780 0.150 0.551 0.01 MinMaxScaler()   0 -> 0.6139563041776123
 1015/3780 0.150 0.551 0.03162277660168379 StandardScaler() 0 -> 0.49130929896994974
 1016/3780 0.150 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5631308291766703
 1017/3780 0.150 0.551 0.1 StandardScaler() 0 -> 0.4765916627551415
 1018/3780 0.150 0.551 0.1 MinMaxScaler()   0 -> 0.5400579979493544
 1019/3780 0.150 0.551 0.31622776601683794 StandardScaler() 0 -> 0.47010283430693756
 1020/3780 0.150 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5081980350265612
 1021/3780 0.150 0.551 1.0 StandardScaler() 0 -> 0.5286017987844219
 1022/3780 0.150 0.551 1.0 MinMaxScaler()   0 -> 0.49104389493679196
 1023/3780 0.150 0.700 scale StandardScaler() 0 -> 0.47620811153385717
 1024/3780 0.150 0.700 scale MinMaxScaler()   0 -> 0.47843846661760886
 1025/3780 0.150 0.700 auto StandardScaler() 0 -> 0.4762081115338573
 1026/3780 0.150 0.700 auto MinMaxScaler()   0 -> 0.5392703458392427
 1027/3780 0.150 0.700 0.01 StandardScaler() 0 -> 0.5068691170163052
 1028/3780 0.150 0.700 0.01 MinMaxScaler()   0 -> 0.6001648241830132
 1029/3780 0.150 0.700 0.03162277660168379 StandardScaler() 0 -> 0.4898990756016975
 1030/3780 0.150 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5593605929730693
 1031/3780 0.150 0.700 0.1 StandardScaler() 0 -> 0.4753741855538942
 1032/3780 0.150 0.700 0.1 MinMaxScaler()   0 -> 0.5366654296896606
 1033/3780 0.150 0.700 0.31622776601683794 StandardScaler() 0 -> 0.4668009175188874
 1034/3780 0.150 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5054171284667223
 1035/3780 0.150 0.700 1.0 StandardScaler() 0 -> 0.5121066390561443
 1036/3780 0.150 0.700 1.0 MinMaxScaler()   0 -> 0.4897732225113205
 1037/3780 0.150 0.888 scale StandardScaler() 0 -> 0.47506860372145693
 1038/3780 0.150 0.888 scale MinMaxScaler()   0 -> 0.47749814392551665
 1039/3780 0.150 0.888 auto StandardScaler() 0 -> 0.4750686037214565
 1040/3780 0.150 0.888 auto MinMaxScaler()   0 -> 0.5358562435248725
 1041/3780 0.150 0.888 0.01 StandardScaler() 0 -> 0.5038152104119371
 1042/3780 0.150 0.888 0.01 MinMaxScaler()   0 -> 0.5876570708650833
 1043/3780 0.150 0.888 0.03162277660168379 StandardScaler() 0 -> 0.48799791936127707
 1044/3780 0.150 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5553591827595459
 1045/3780 0.150 0.888 0.1 StandardScaler() 0 -> 0.4741118939815085
 1046/3780 0.150 0.888 0.1 MinMaxScaler()   0 -> 0.533020349052915
 1047/3780 0.150 0.888 0.31622776601683794 StandardScaler() 0 -> 0.46494528882896297
 1048/3780 0.150 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5027297991048522
 1049/3780 0.150 0.888 1.0 StandardScaler() 0 -> 0.4999584746889489
 1050/3780 0.150 0.888 1.0 MinMaxScaler()   0 -> 0.48814899038926035
 1051/3780 0.150 1.126 scale StandardScaler() 0 -> 0.4741280987017496
 1052/3780 0.150 1.126 scale MinMaxScaler()   0 -> 0.47685615420350186
 1053/3780 0.150 1.126 auto StandardScaler() 0 -> 0.4741280987017494
 1054/3780 0.150 1.126 auto MinMaxScaler()   0 -> 0.5320580436337722
 1055/3780 0.150 1.126 0.01 StandardScaler() 0 -> 0.5014960456916672
 1056/3780 0.150 1.126 0.01 MinMaxScaler()   0 -> 0.5772930162900732
 1057/3780 0.150 1.126 0.03162277660168379 StandardScaler() 0 -> 0.4860234378700706
 1058/3780 0.150 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5530238104825556
 1059/3780 0.150 1.126 0.1 StandardScaler() 0 -> 0.47337652020257526
 1060/3780 0.150 1.126 0.1 MinMaxScaler()   0 -> 0.5293101694457102
 1061/3780 0.150 1.126 0.31622776601683794 StandardScaler() 0 -> 0.46518817133613705
 1062/3780 0.150 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.49987317805787645
 1063/3780 0.150 1.126 1.0 StandardScaler() 0 -> 0.49393624417801735
 1064/3780 0.150 1.126 1.0 MinMaxScaler()   0 -> 0.4864931342112741
 1065/3780 0.150 1.429 scale StandardScaler() 0 -> 0.473845116067809
 1066/3780 0.150 1.429 scale MinMaxScaler()   0 -> 0.47570525486931636
 1067/3780 0.150 1.429 auto StandardScaler() 0 -> 0.4738451160678088
 1068/3780 0.150 1.429 auto MinMaxScaler()   0 -> 0.5286756661095272
 1069/3780 0.150 1.429 0.01 StandardScaler() 0 -> 0.500008255948483
 1070/3780 0.150 1.429 0.01 MinMaxScaler()   0 -> 0.5689617661727189
 1071/3780 0.150 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48477423628153066
 1072/3780 0.150 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5509841268976484
 1073/3780 0.150 1.429 0.1 StandardScaler() 0 -> 0.4731963817612281
 1074/3780 0.150 1.429 0.1 MinMaxScaler()   0 -> 0.5264318796521769
 1075/3780 0.150 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4663550807703242
 1076/3780 0.150 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.4983834894852628
 1077/3780 0.150 1.429 1.0 StandardScaler() 0 -> 0.4903897296278574
 1078/3780 0.150 1.429 1.0 MinMaxScaler()   0 -> 0.4849197355209685
 1079/3780 0.150 1.814 scale StandardScaler() 0 -> 0.47393278267081573
 1080/3780 0.150 1.814 scale MinMaxScaler()   0 -> 0.4745440121093978
 1081/3780 0.150 1.814 auto StandardScaler() 0 -> 0.47393278267081557
 1082/3780 0.150 1.814 auto MinMaxScaler()   0 -> 0.5257241380004406
 1083/3780 0.150 1.814 0.01 StandardScaler() 0 -> 0.49839751872294763
 1084/3780 0.150 1.814 0.01 MinMaxScaler()   0 -> 0.5636146794814884
 1085/3780 0.150 1.814 0.03162277660168379 StandardScaler() 0 -> 0.48325191141179463
 1086/3780 0.150 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5492530529837013
 1087/3780 0.150 1.814 0.1 StandardScaler() 0 -> 0.472654340663851
 1088/3780 0.150 1.814 0.1 MinMaxScaler()   0 -> 0.5227572386450999
 1089/3780 0.150 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4679767149079122
 1090/3780 0.150 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.49710085945383736
 1091/3780 0.150 1.814 1.0 StandardScaler() 0 -> 0.4885890337742673
 1092/3780 0.150 1.814 1.0 MinMaxScaler()   0 -> 0.4840312724380596
 1093/3780 0.150 2.302 scale StandardScaler() 0 -> 0.47356650075457324
 1094/3780 0.150 2.302 scale MinMaxScaler()   0 -> 0.47368789227859986
 1095/3780 0.150 2.302 auto StandardScaler() 0 -> 0.47356650075457346
 1096/3780 0.150 2.302 auto MinMaxScaler()   0 -> 0.5219685704930238
 1097/3780 0.150 2.302 0.01 StandardScaler() 0 -> 0.497990289114262
 1098/3780 0.150 2.302 0.01 MinMaxScaler()   0 -> 0.5600387096179099
 1099/3780 0.150 2.302 0.03162277660168379 StandardScaler() 0 -> 0.48254159866203644
 1100/3780 0.150 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5471867358232555
 1101/3780 0.150 2.302 0.1 StandardScaler() 0 -> 0.4725680716120635
 1102/3780 0.150 2.302 0.1 MinMaxScaler()   0 -> 0.5192109362142929
 1103/3780 0.150 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4711691148711598
 1104/3780 0.150 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.49648483656858006
 1105/3780 0.150 2.302 1.0 StandardScaler() 0 -> 0.4884315364546543
 1106/3780 0.150 2.302 1.0 MinMaxScaler()   0 -> 0.4829000024974053
 1107/3780 0.150 2.921 scale StandardScaler() 0 -> 0.4736815642340407
 1108/3780 0.150 2.921 scale MinMaxScaler()   0 -> 0.4735714053598028
 1109/3780 0.150 2.921 auto StandardScaler() 0 -> 0.47368156423404023
 1110/3780 0.150 2.921 auto MinMaxScaler()   0 -> 0.5183344564679059
 1111/3780 0.150 2.921 0.01 StandardScaler() 0 -> 0.4972386512658655
 1112/3780 0.150 2.921 0.01 MinMaxScaler()   0 -> 0.5570602955058117
 1113/3780 0.150 2.921 0.03162277660168379 StandardScaler() 0 -> 0.48240403605163135
 1114/3780 0.150 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5453606538180708
 1115/3780 0.150 2.921 0.1 StandardScaler() 0 -> 0.4742012899802497
 1116/3780 0.150 2.921 0.1 MinMaxScaler()   0 -> 0.5159551364261725
 1117/3780 0.150 2.921 0.31622776601683794 StandardScaler() 0 -> 0.47663389832489506
 1118/3780 0.150 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.4961893059785439
 1119/3780 0.150 2.921 1.0 StandardScaler() 0 -> 0.48820614022722736
 1120/3780 0.150 2.921 1.0 MinMaxScaler()   0 -> 0.4823146552400221
 1121/3780 0.150 3.707 scale StandardScaler() 0 -> 0.47572586670126754
 1122/3780 0.150 3.707 scale MinMaxScaler()   0 -> 0.4742109734459188
 1123/3780 0.150 3.707 auto StandardScaler() 0 -> 0.4757258667012678
 1124/3780 0.150 3.707 auto MinMaxScaler()   0 -> 0.5152295809772239
 1125/3780 0.150 3.707 0.01 StandardScaler() 0 -> 0.4967371894405825
 1126/3780 0.150 3.707 0.01 MinMaxScaler()   0 -> 0.5553590244950554
 1127/3780 0.150 3.707 0.03162277660168379 StandardScaler() 0 -> 0.4816737489570803
 1128/3780 0.150 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5428604944241949
 1129/3780 0.150 3.707 0.1 StandardScaler() 0 -> 0.4769689831392863
 1130/3780 0.150 3.707 0.1 MinMaxScaler()   0 -> 0.5126870373842591
 1131/3780 0.150 3.707 0.31622776601683794 StandardScaler() 0 -> 0.48237699376053955
 1132/3780 0.150 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4957786898593471
 1133/3780 0.150 3.707 1.0 StandardScaler() 0 -> 0.4882424878098293
 1134/3780 0.150 3.707 1.0 MinMaxScaler()   0 -> 0.4815579548966083
 1135/3780 0.150 4.703 scale StandardScaler() 0 -> 0.4788545795754562
 1136/3780 0.150 4.703 scale MinMaxScaler()   0 -> 0.4766535364704172
 1137/3780 0.150 4.703 auto StandardScaler() 0 -> 0.4788545795754556
 1138/3780 0.150 4.703 auto MinMaxScaler()   0 -> 0.5120574789766281
 1139/3780 0.150 4.703 0.01 StandardScaler() 0 -> 0.49589769608582124
 1140/3780 0.150 4.703 0.01 MinMaxScaler()   0 -> 0.5541923837771088
 1141/3780 0.150 4.703 0.03162277660168379 StandardScaler() 0 -> 0.48147471908636846
 1142/3780 0.150 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5406160028596335
 1143/3780 0.150 4.703 0.1 StandardScaler() 0 -> 0.4792965626482035
 1144/3780 0.150 4.703 0.1 MinMaxScaler()   0 -> 0.5099254359531572
 1145/3780 0.150 4.703 0.31622776601683794 StandardScaler() 0 -> 0.48740977716021444
 1146/3780 0.150 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.4952169876316442
 1147/3780 0.150 4.703 1.0 StandardScaler() 0 -> 0.4883692277472916
 1148/3780 0.150 4.703 1.0 MinMaxScaler()   0 -> 0.48139013930488717
 1149/3780 0.150 5.968 scale StandardScaler() 0 -> 0.4813761836558466
 1150/3780 0.150 5.968 scale MinMaxScaler()   0 -> 0.47981296629022924
 1151/3780 0.150 5.968 auto StandardScaler() 0 -> 0.4813761836558467
 1152/3780 0.150 5.968 auto MinMaxScaler()   0 -> 0.5094927474862979
 1153/3780 0.150 5.968 0.01 StandardScaler() 0 -> 0.4959510383682107
 1154/3780 0.150 5.968 0.01 MinMaxScaler()   0 -> 0.5532817389598674
 1155/3780 0.150 5.968 0.03162277660168379 StandardScaler() 0 -> 0.48183569020548545
 1156/3780 0.150 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5379071703112998
 1157/3780 0.150 5.968 0.1 StandardScaler() 0 -> 0.4832420267286275
 1158/3780 0.150 5.968 0.1 MinMaxScaler()   0 -> 0.5076399164407766
 1159/3780 0.150 5.968 0.31622776601683794 StandardScaler() 0 -> 0.49424252154256904
 1160/3780 0.150 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.4943398867760133
 1161/3780 0.150 5.968 1.0 StandardScaler() 0 -> 0.4885410293563699
 1162/3780 0.150 5.968 1.0 MinMaxScaler()   0 -> 0.48174328510264885
 1163/3780 0.150 7.574 scale StandardScaler() 0 -> 0.48593350110634764
 1164/3780 0.150 7.574 scale MinMaxScaler()   0 -> 0.4823192075323564
 1165/3780 0.150 7.574 auto StandardScaler() 0 -> 0.48593350110634853
 1166/3780 0.150 7.574 auto MinMaxScaler()   0 -> 0.5072800507215756
 1167/3780 0.150 7.574 0.01 StandardScaler() 0 -> 0.4953923203229891
 1168/3780 0.150 7.574 0.01 MinMaxScaler()   0 -> 0.5522212974477094
 1169/3780 0.150 7.574 0.03162277660168379 StandardScaler() 0 -> 0.48182574582291626
 1170/3780 0.150 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5348200812613879
 1171/3780 0.150 7.574 0.1 StandardScaler() 0 -> 0.48826781782256123
 1172/3780 0.150 7.574 0.1 MinMaxScaler()   0 -> 0.5045179162113432
 1173/3780 0.150 7.574 0.31622776601683794 StandardScaler() 0 -> 0.503011010891877
 1174/3780 0.150 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.49394024884101534
 1175/3780 0.150 7.574 1.0 StandardScaler() 0 -> 0.4886696413631289
 1176/3780 0.150 7.574 1.0 MinMaxScaler()   0 -> 0.4814535712728094
 1177/3780 0.150 9.611 scale StandardScaler() 0 -> 0.4917389273368058
 1178/3780 0.150 9.611 scale MinMaxScaler()   0 -> 0.4852129164659556
 1179/3780 0.150 9.611 auto StandardScaler() 0 -> 0.49173892733680774
 1180/3780 0.150 9.611 auto MinMaxScaler()   0 -> 0.5040317701365378
 1181/3780 0.150 9.611 0.01 StandardScaler() 0 -> 0.4944212197921128
 1182/3780 0.150 9.611 0.01 MinMaxScaler()   0 -> 0.5512628090042445
 1183/3780 0.150 9.611 0.03162277660168379 StandardScaler() 0 -> 0.48092988380549023
 1184/3780 0.150 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5312613107476654
 1185/3780 0.150 9.611 0.1 StandardScaler() 0 -> 0.4959742293404714
 1186/3780 0.150 9.611 0.1 MinMaxScaler()   0 -> 0.5025388628902671
 1187/3780 0.150 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5105053063889197
 1188/3780 0.150 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4930951734257918
 1189/3780 0.150 9.611 1.0 StandardScaler() 0 -> 0.488710254650311
 1190/3780 0.150 9.611 1.0 MinMaxScaler()   0 -> 0.481939767472183
 1191/3780 0.150 12.196 scale StandardScaler() 0 -> 0.49972191327209964
 1192/3780 0.150 12.196 scale MinMaxScaler()   0 -> 0.48852174749908933
 1193/3780 0.150 12.196 auto StandardScaler() 0 -> 0.4997219132721004
 1194/3780 0.150 12.196 auto MinMaxScaler()   0 -> 0.5023740888189695
 1195/3780 0.150 12.196 0.01 StandardScaler() 0 -> 0.4939397715184546
 1196/3780 0.150 12.196 0.01 MinMaxScaler()   0 -> 0.5507406929324437
 1197/3780 0.150 12.196 0.03162277660168379 StandardScaler() 0 -> 0.48125016759465417
 1198/3780 0.150 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5282000943044153
 1199/3780 0.150 12.196 0.1 StandardScaler() 0 -> 0.5070863176788782
 1200/3780 0.150 12.196 0.1 MinMaxScaler()   0 -> 0.50076218602113
 1201/3780 0.150 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5163236624397057
 1202/3780 0.150 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.49226051009954697
 1203/3780 0.150 12.196 1.0 StandardScaler() 0 -> 0.48871422035331946
 1204/3780 0.150 12.196 1.0 MinMaxScaler()   0 -> 0.4821093595667448
 1205/3780 0.150 15.476 scale StandardScaler() 0 -> 0.5111478216733605
 1206/3780 0.150 15.476 scale MinMaxScaler()   0 -> 0.49346281133973663
 1207/3780 0.150 15.476 auto StandardScaler() 0 -> 0.5111478216733557
 1208/3780 0.150 15.476 auto MinMaxScaler()   0 -> 0.5007960456276991
 1209/3780 0.150 15.476 0.01 StandardScaler() 0 -> 0.49337378767630535
 1210/3780 0.150 15.476 0.01 MinMaxScaler()   0 -> 0.5491164630435109
 1211/3780 0.150 15.476 0.03162277660168379 StandardScaler() 0 -> 0.4822551261874499
 1212/3780 0.150 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5248479128961251
 1213/3780 0.150 15.476 0.1 StandardScaler() 0 -> 0.5226318370516182
 1214/3780 0.150 15.476 0.1 MinMaxScaler()   0 -> 0.5003778016870123
 1215/3780 0.150 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5203974005081241
 1216/3780 0.150 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.4912548867216282
 1217/3780 0.150 15.476 1.0 StandardScaler() 0 -> 0.48871009192522247
 1218/3780 0.150 15.476 1.0 MinMaxScaler()   0 -> 0.48219504514400574
 1219/3780 0.150 19.638 scale StandardScaler() 0 -> 0.5269302336440983
 1220/3780 0.150 19.638 scale MinMaxScaler()   0 -> 0.5022250955335424
 1221/3780 0.150 19.638 auto StandardScaler() 0 -> 0.5269302336441016
 1222/3780 0.150 19.638 auto MinMaxScaler()   0 -> 0.5005627669241635
 1223/3780 0.150 19.638 0.01 StandardScaler() 0 -> 0.49204432329627484
 1224/3780 0.150 19.638 0.01 MinMaxScaler()   0 -> 0.547908396431011
 1225/3780 0.150 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4834150775372092
 1226/3780 0.150 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5209607329536184
 1227/3780 0.150 19.638 0.1 StandardScaler() 0 -> 0.541733314711514
 1228/3780 0.150 19.638 0.1 MinMaxScaler()   0 -> 0.49995610896501375
 1229/3780 0.150 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5241274153121327
 1230/3780 0.150 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.49004645268029307
 1231/3780 0.150 19.638 1.0 StandardScaler() 0 -> 0.48871009192522247
 1232/3780 0.150 19.638 1.0 MinMaxScaler()   0 -> 0.4832969902417346
 1233/3780 0.150 24.920 scale StandardScaler() 0 -> 0.547086345329965
 1234/3780 0.150 24.920 scale MinMaxScaler()   0 -> 0.5133794953464874
 1235/3780 0.150 24.920 auto StandardScaler() 0 -> 0.5470863453299581
 1236/3780 0.150 24.920 auto MinMaxScaler()   0 -> 0.5000664824532209
 1237/3780 0.150 24.920 0.01 StandardScaler() 0 -> 0.49003321586960147
 1238/3780 0.150 24.920 0.01 MinMaxScaler()   0 -> 0.5461595561719338
 1239/3780 0.150 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4853173860413204
 1240/3780 0.150 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5179741321874974
 1241/3780 0.150 24.920 0.1 StandardScaler() 0 -> 0.5638246911999372
 1242/3780 0.150 24.920 0.1 MinMaxScaler()   0 -> 0.49923941015891743
 1243/3780 0.150 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5276552466884729
 1244/3780 0.150 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.488747246711483
 1245/3780 0.150 24.920 1.0 StandardScaler() 0 -> 0.48871009192522247
 1246/3780 0.150 24.920 1.0 MinMaxScaler()   0 -> 0.4847175687161294
 1247/3780 0.150 31.623 scale StandardScaler() 0 -> 0.5704162690401869
 1248/3780 0.150 31.623 scale MinMaxScaler()   0 -> 0.5273937401879646
 1249/3780 0.150 31.623 auto StandardScaler() 0 -> 0.5704162690401927
 1250/3780 0.150 31.623 auto MinMaxScaler()   0 -> 0.4992575660451018
 1251/3780 0.150 31.623 0.01 StandardScaler() 0 -> 0.48857156388149026
 1252/3780 0.150 31.623 0.01 MinMaxScaler()   0 -> 0.5443920052745889
 1253/3780 0.150 31.623 0.03162277660168379 StandardScaler() 0 -> 0.48870376794496656
 1254/3780 0.150 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5148745645808558
 1255/3780 0.150 31.623 0.1 StandardScaler() 0 -> 0.5882653921286694
 1256/3780 0.150 31.623 0.1 MinMaxScaler()   0 -> 0.498485351361548
 1257/3780 0.150 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5300451822179655
 1258/3780 0.150 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4880272575023242
 1259/3780 0.150 31.623 1.0 StandardScaler() 0 -> 0.48871009192522247
 1260/3780 0.150 31.623 1.0 MinMaxScaler()   0 -> 0.48644511029465604
 1261/3780 0.200 0.032 scale StandardScaler() 0 -> 0.5289264495541057
 1262/3780 0.200 0.032 scale MinMaxScaler()   0 -> 0.5221453597386172
 1263/3780 0.200 0.032 auto StandardScaler() 0 -> 0.5289264495541058
 1264/3780 0.200 0.032 auto MinMaxScaler()   0 -> 0.6384120029905612
 1265/3780 0.200 0.032 0.01 StandardScaler() 0 -> 0.5889580862260789
 1266/3780 0.200 0.032 0.01 MinMaxScaler()   0 -> 0.7444051417895602
 1267/3780 0.200 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5403753106766717
 1268/3780 0.200 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7001878678398309
 1269/3780 0.200 0.032 0.1 StandardScaler() 0 -> 0.5305641173906068
 1270/3780 0.200 0.032 0.1 MinMaxScaler()   0 -> 0.6341035875754382
 1271/3780 0.200 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6048244302871563
 1272/3780 0.200 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5761393243305757
 1273/3780 0.200 0.032 1.0 StandardScaler() 0 -> 0.7373263573770837
 1274/3780 0.200 0.032 1.0 MinMaxScaler()   0 -> 0.5317018484040312
 1275/3780 0.200 0.040 scale StandardScaler() 0 -> 0.5199143453668541
 1276/3780 0.200 0.040 scale MinMaxScaler()   0 -> 0.515115182549114
 1277/3780 0.200 0.040 auto StandardScaler() 0 -> 0.5199143453668541
 1278/3780 0.200 0.040 auto MinMaxScaler()   0 -> 0.6271476198279936
 1279/3780 0.200 0.040 0.01 StandardScaler() 0 -> 0.5766947749470853
 1280/3780 0.200 0.040 0.01 MinMaxScaler()   0 -> 0.738228469660076
 1281/3780 0.200 0.040 0.03162277660168379 StandardScaler() 0 -> 0.530836682760326
 1282/3780 0.200 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.6853784870947722
 1283/3780 0.200 0.040 0.1 StandardScaler() 0 -> 0.5213176663770084
 1284/3780 0.200 0.040 0.1 MinMaxScaler()   0 -> 0.6220528037613585
 1285/3780 0.200 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5870605791810163
 1286/3780 0.200 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5643417086071182
 1287/3780 0.200 0.040 1.0 StandardScaler() 0 -> 0.7293707031877082
 1288/3780 0.200 0.040 1.0 MinMaxScaler()   0 -> 0.5248613701634226
 1289/3780 0.200 0.051 scale StandardScaler() 0 -> 0.512207140895094
 1290/3780 0.200 0.051 scale MinMaxScaler()   0 -> 0.5092030094571908
 1291/3780 0.200 0.051 auto StandardScaler() 0 -> 0.512207140895094
 1292/3780 0.200 0.051 auto MinMaxScaler()   0 -> 0.6140637548695614
 1293/3780 0.200 0.051 0.01 StandardScaler() 0 -> 0.5653436295600343
 1294/3780 0.200 0.051 0.01 MinMaxScaler()   0 -> 0.730622858120828
 1295/3780 0.200 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5243643723590026
 1296/3780 0.200 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.669329955310198
 1297/3780 0.200 0.051 0.1 StandardScaler() 0 -> 0.5134026274045879
 1298/3780 0.200 0.051 0.1 MinMaxScaler()   0 -> 0.6093719532105119
 1299/3780 0.200 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5703464538881268
 1300/3780 0.200 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5540577125521019
 1301/3780 0.200 0.051 1.0 StandardScaler() 0 -> 0.7195838023163431
 1302/3780 0.200 0.051 1.0 MinMaxScaler()   0 -> 0.5190376366611161
 1303/3780 0.200 0.065 scale StandardScaler() 0 -> 0.5063056781439742
 1304/3780 0.200 0.065 scale MinMaxScaler()   0 -> 0.5043516978069668
 1305/3780 0.200 0.065 auto StandardScaler() 0 -> 0.5063056781439742
 1306/3780 0.200 0.065 auto MinMaxScaler()   0 -> 0.6010077070284446
 1307/3780 0.200 0.065 0.01 StandardScaler() 0 -> 0.5562481745940379
 1308/3780 0.200 0.065 0.01 MinMaxScaler()   0 -> 0.7213189727550651
 1309/3780 0.200 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5178127381946701
 1310/3780 0.200 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6544255889921954
 1311/3780 0.200 0.065 0.1 StandardScaler() 0 -> 0.5071907518387196
 1312/3780 0.200 0.065 0.1 MinMaxScaler()   0 -> 0.5959377713063903
 1313/3780 0.200 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5553407690496067
 1314/3780 0.200 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.546972440696833
 1315/3780 0.200 0.065 1.0 StandardScaler() 0 -> 0.7076619978027049
 1316/3780 0.200 0.065 1.0 MinMaxScaler()   0 -> 0.5138020843569768
 1317/3780 0.200 0.082 scale StandardScaler() 0 -> 0.5012275276584343
 1318/3780 0.200 0.082 scale MinMaxScaler()   0 -> 0.5002762364904975
 1319/3780 0.200 0.082 auto StandardScaler() 0 -> 0.5012275276584343
 1320/3780 0.200 0.082 auto MinMaxScaler()   0 -> 0.5878213695240767
 1321/3780 0.200 0.082 0.01 StandardScaler() 0 -> 0.5484633164913756
 1322/3780 0.200 0.082 0.01 MinMaxScaler()   0 -> 0.7101320339214604
 1323/3780 0.200 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5124978413554663
 1324/3780 0.200 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6420144433590127
 1325/3780 0.200 0.082 0.1 StandardScaler() 0 -> 0.5017151651574899
 1326/3780 0.200 0.082 0.1 MinMaxScaler()   0 -> 0.5831220793672043
 1327/3780 0.200 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5418434117600643
 1328/3780 0.200 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.540932257974457
 1329/3780 0.200 0.082 1.0 StandardScaler() 0 -> 0.6933763161084453
 1330/3780 0.200 0.082 1.0 MinMaxScaler()   0 -> 0.509660484228209
 1331/3780 0.200 0.104 scale StandardScaler() 0 -> 0.4964613754871632
 1332/3780 0.200 0.104 scale MinMaxScaler()   0 -> 0.49626491017041774
 1333/3780 0.200 0.104 auto StandardScaler() 0 -> 0.4964613754871631
 1334/3780 0.200 0.104 auto MinMaxScaler()   0 -> 0.5759427308625459
 1335/3780 0.200 0.104 0.01 StandardScaler() 0 -> 0.5418265992370855
 1336/3780 0.200 0.104 0.01 MinMaxScaler()   0 -> 0.6969232324151496
 1337/3780 0.200 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5073684246140354
 1338/3780 0.200 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.63096445747967
 1339/3780 0.200 0.104 0.1 StandardScaler() 0 -> 0.49667663461559936
 1340/3780 0.200 0.104 0.1 MinMaxScaler()   0 -> 0.5720319774054755
 1341/3780 0.200 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5294628612797639
 1342/3780 0.200 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5359778347956864
 1343/3780 0.200 0.104 1.0 StandardScaler() 0 -> 0.6769705623990956
 1344/3780 0.200 0.104 1.0 MinMaxScaler()   0 -> 0.5056936956629526
 1345/3780 0.200 0.132 scale StandardScaler() 0 -> 0.4921100015676651
 1346/3780 0.200 0.132 scale MinMaxScaler()   0 -> 0.4923461780251444
 1347/3780 0.200 0.132 auto StandardScaler() 0 -> 0.4921100015676651
 1348/3780 0.200 0.132 auto MinMaxScaler()   0 -> 0.5668003221645889
 1349/3780 0.200 0.132 0.01 StandardScaler() 0 -> 0.5362052307664923
 1350/3780 0.200 0.132 0.01 MinMaxScaler()   0 -> 0.6817151346654513
 1351/3780 0.200 0.132 0.03162277660168379 StandardScaler() 0 -> 0.503407747195489
 1352/3780 0.200 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.617797540211445
 1353/3780 0.200 0.132 0.1 StandardScaler() 0 -> 0.49219504684192544
 1354/3780 0.200 0.132 0.1 MinMaxScaler()   0 -> 0.5638499147084979
 1355/3780 0.200 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5178524784088483
 1356/3780 0.200 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5312911154480937
 1357/3780 0.200 0.132 1.0 StandardScaler() 0 -> 0.6586805560770287
 1358/3780 0.200 0.132 1.0 MinMaxScaler()   0 -> 0.5021882462185582
 1359/3780 0.200 0.168 scale StandardScaler() 0 -> 0.4883646779791841
 1360/3780 0.200 0.168 scale MinMaxScaler()   0 -> 0.4898528630672116
 1361/3780 0.200 0.168 auto StandardScaler() 0 -> 0.48836467797918415
 1362/3780 0.200 0.168 auto MinMaxScaler()   0 -> 0.560319850246182
 1363/3780 0.200 0.168 0.01 StandardScaler() 0 -> 0.5312401820775237
 1364/3780 0.200 0.168 0.01 MinMaxScaler()   0 -> 0.6657764217558019
 1365/3780 0.200 0.168 0.03162277660168379 StandardScaler() 0 -> 0.49991303497557044
 1366/3780 0.200 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.605427046521508
 1367/3780 0.200 0.168 0.1 StandardScaler() 0 -> 0.4880736965541281
 1368/3780 0.200 0.168 0.1 MinMaxScaler()   0 -> 0.5579097727323129
 1369/3780 0.200 0.168 0.31622776601683794 StandardScaler() 0 -> 0.507081144251852
 1370/3780 0.200 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5266622166923401
 1371/3780 0.200 0.168 1.0 StandardScaler() 0 -> 0.6383277302393541
 1372/3780 0.200 0.168 1.0 MinMaxScaler()   0 -> 0.499150200259288
 1373/3780 0.200 0.213 scale StandardScaler() 0 -> 0.485389047667663
 1374/3780 0.200 0.213 scale MinMaxScaler()   0 -> 0.48741087141547185
 1375/3780 0.200 0.213 auto StandardScaler() 0 -> 0.48538904766766305
 1376/3780 0.200 0.213 auto MinMaxScaler()   0 -> 0.5554960222173632
 1377/3780 0.200 0.213 0.01 StandardScaler() 0 -> 0.5270024507648171
 1378/3780 0.200 0.213 0.01 MinMaxScaler()   0 -> 0.6518064196994998
 1379/3780 0.200 0.213 0.03162277660168379 StandardScaler() 0 -> 0.49692972744022956
 1380/3780 0.200 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5923561685563532
 1381/3780 0.200 0.213 0.1 StandardScaler() 0 -> 0.4849098087591035
 1382/3780 0.200 0.213 0.1 MinMaxScaler()   0 -> 0.5531886789797156
 1383/3780 0.200 0.213 0.31622776601683794 StandardScaler() 0 -> 0.4974412589265409
 1384/3780 0.200 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.52283152841956
 1385/3780 0.200 0.213 1.0 StandardScaler() 0 -> 0.6167120340381437
 1386/3780 0.200 0.213 1.0 MinMaxScaler()   0 -> 0.4963275754506073
 1387/3780 0.200 0.270 scale StandardScaler() 0 -> 0.4828437397023053
 1388/3780 0.200 0.270 scale MinMaxScaler()   0 -> 0.4849616564109209
 1389/3780 0.200 0.270 auto StandardScaler() 0 -> 0.48284373970230526
 1390/3780 0.200 0.270 auto MinMaxScaler()   0 -> 0.5511974489565095
 1391/3780 0.200 0.270 0.01 StandardScaler() 0 -> 0.5228531576532929
 1392/3780 0.200 0.270 0.01 MinMaxScaler()   0 -> 0.6392044478936647
 1393/3780 0.200 0.270 0.03162277660168379 StandardScaler() 0 -> 0.49495862990524264
 1394/3780 0.200 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5805376079256995
 1395/3780 0.200 0.270 0.1 StandardScaler() 0 -> 0.48202730170277697
 1396/3780 0.200 0.270 0.1 MinMaxScaler()   0 -> 0.5491991618596536
 1397/3780 0.200 0.270 0.31622776601683794 StandardScaler() 0 -> 0.4885376172617834
 1398/3780 0.200 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5190977053531746
 1399/3780 0.200 0.270 1.0 StandardScaler() 0 -> 0.5949448670567801
 1400/3780 0.200 0.270 1.0 MinMaxScaler()   0 -> 0.49430049728241343
 1401/3780 0.200 0.342 scale StandardScaler() 0 -> 0.4802059262114189
 1402/3780 0.200 0.342 scale MinMaxScaler()   0 -> 0.4825828483360132
 1403/3780 0.200 0.342 auto StandardScaler() 0 -> 0.48020592621141905
 1404/3780 0.200 0.342 auto MinMaxScaler()   0 -> 0.5475580716283711
 1405/3780 0.200 0.342 0.01 StandardScaler() 0 -> 0.5181040532103732
 1406/3780 0.200 0.342 0.01 MinMaxScaler()   0 -> 0.6280862644676154
 1407/3780 0.200 0.342 0.03162277660168379 StandardScaler() 0 -> 0.493033520005856
 1408/3780 0.200 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5712430729391575
 1409/3780 0.200 0.342 0.1 StandardScaler() 0 -> 0.47973574908165784
 1410/3780 0.200 0.342 0.1 MinMaxScaler()   0 -> 0.54569729861093
 1411/3780 0.200 0.342 0.31622776601683794 StandardScaler() 0 -> 0.48133671490963764
 1412/3780 0.200 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5155079524354842
 1413/3780 0.200 0.342 1.0 StandardScaler() 0 -> 0.5729633376863376
 1414/3780 0.200 0.342 1.0 MinMaxScaler()   0 -> 0.4920205858904388
 1415/3780 0.200 0.434 scale StandardScaler() 0 -> 0.478497284227637
 1416/3780 0.200 0.434 scale MinMaxScaler()   0 -> 0.48051362154585014
 1417/3780 0.200 0.434 auto StandardScaler() 0 -> 0.478497284227637
 1418/3780 0.200 0.434 auto MinMaxScaler()   0 -> 0.5445291897294926
 1419/3780 0.200 0.434 0.01 StandardScaler() 0 -> 0.5142101421276076
 1420/3780 0.200 0.434 0.01 MinMaxScaler()   0 -> 0.6150550765299507
 1421/3780 0.200 0.434 0.03162277660168379 StandardScaler() 0 -> 0.4908738560625394
 1422/3780 0.200 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5648202144458662
 1423/3780 0.200 0.434 0.1 StandardScaler() 0 -> 0.47792124518527374
 1424/3780 0.200 0.434 0.1 MinMaxScaler()   0 -> 0.5424500456440269
 1425/3780 0.200 0.434 0.31622776601683794 StandardScaler() 0 -> 0.4752620935435889
 1426/3780 0.200 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5123722273894288
 1427/3780 0.200 0.434 1.0 StandardScaler() 0 -> 0.5523739533678936
 1428/3780 0.200 0.434 1.0 MinMaxScaler()   0 -> 0.49015547992037095
 1429/3780 0.200 0.551 scale StandardScaler() 0 -> 0.47655289716749144
 1430/3780 0.200 0.551 scale MinMaxScaler()   0 -> 0.4782009564142931
 1431/3780 0.200 0.551 auto StandardScaler() 0 -> 0.4765528971674915
 1432/3780 0.200 0.551 auto MinMaxScaler()   0 -> 0.541559193764856
 1433/3780 0.200 0.551 0.01 StandardScaler() 0 -> 0.5107968181122979
 1434/3780 0.200 0.551 0.01 MinMaxScaler()   0 -> 0.6027114183820544
 1435/3780 0.200 0.551 0.03162277660168379 StandardScaler() 0 -> 0.48951945926307266
 1436/3780 0.200 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5605845833989139
 1437/3780 0.200 0.551 0.1 StandardScaler() 0 -> 0.4759212696785171
 1438/3780 0.200 0.551 0.1 MinMaxScaler()   0 -> 0.5392836873669132
 1439/3780 0.200 0.551 0.31622776601683794 StandardScaler() 0 -> 0.47044155111135316
 1440/3780 0.200 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5091044968478544
 1441/3780 0.200 0.551 1.0 StandardScaler() 0 -> 0.5332133408473934
 1442/3780 0.200 0.551 1.0 MinMaxScaler()   0 -> 0.4887757677621087
 1443/3780 0.200 0.700 scale StandardScaler() 0 -> 0.47507294318974314
 1444/3780 0.200 0.700 scale MinMaxScaler()   0 -> 0.47642146091828524
 1445/3780 0.200 0.700 auto StandardScaler() 0 -> 0.4750729431897432
 1446/3780 0.200 0.700 auto MinMaxScaler()   0 -> 0.538439052308754
 1447/3780 0.200 0.700 0.01 StandardScaler() 0 -> 0.508134533384574
 1448/3780 0.200 0.700 0.01 MinMaxScaler()   0 -> 0.5899544550728798
 1449/3780 0.200 0.700 0.03162277660168379 StandardScaler() 0 -> 0.48823649843026357
 1450/3780 0.200 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5571064055002326
 1451/3780 0.200 0.700 0.1 StandardScaler() 0 -> 0.47429578811916273
 1452/3780 0.200 0.700 0.1 MinMaxScaler()   0 -> 0.5360267971902891
 1453/3780 0.200 0.700 0.31622776601683794 StandardScaler() 0 -> 0.46683467345016777
 1454/3780 0.200 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5058809439150638
 1455/3780 0.200 0.700 1.0 StandardScaler() 0 -> 0.5153334719465917
 1456/3780 0.200 0.700 1.0 MinMaxScaler()   0 -> 0.4871545106980932
 1457/3780 0.200 0.888 scale StandardScaler() 0 -> 0.4740308329087706
 1458/3780 0.200 0.888 scale MinMaxScaler()   0 -> 0.4752141155389313
 1459/3780 0.200 0.888 auto StandardScaler() 0 -> 0.4740308329087708
 1460/3780 0.200 0.888 auto MinMaxScaler()   0 -> 0.5357432781308814
 1461/3780 0.200 0.888 0.01 StandardScaler() 0 -> 0.5052097072069671
 1462/3780 0.200 0.888 0.01 MinMaxScaler()   0 -> 0.5787255595078347
 1463/3780 0.200 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4870426474163913
 1464/3780 0.200 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5541721185678979
 1465/3780 0.200 0.888 0.1 StandardScaler() 0 -> 0.4730817236467117
 1466/3780 0.200 0.888 0.1 MinMaxScaler()   0 -> 0.5334826518586976
 1467/3780 0.200 0.888 0.31622776601683794 StandardScaler() 0 -> 0.4647569252874031
 1468/3780 0.200 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5029604782241067
 1469/3780 0.200 0.888 1.0 StandardScaler() 0 -> 0.5034427119614424
 1470/3780 0.200 0.888 1.0 MinMaxScaler()   0 -> 0.48569521159191215
 1471/3780 0.200 1.126 scale StandardScaler() 0 -> 0.4730974273495605
 1472/3780 0.200 1.126 scale MinMaxScaler()   0 -> 0.4740988821526197
 1473/3780 0.200 1.126 auto StandardScaler() 0 -> 0.47309742734956006
 1474/3780 0.200 1.126 auto MinMaxScaler()   0 -> 0.5328536311344133
 1475/3780 0.200 1.126 0.01 StandardScaler() 0 -> 0.502240397546624
 1476/3780 0.200 1.126 0.01 MinMaxScaler()   0 -> 0.5708257312255216
 1477/3780 0.200 1.126 0.03162277660168379 StandardScaler() 0 -> 0.4854226895609246
 1478/3780 0.200 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5521577231539677
 1479/3780 0.200 1.126 0.1 StandardScaler() 0 -> 0.4715964906877254
 1480/3780 0.200 1.126 0.1 MinMaxScaler()   0 -> 0.5298432902589374
 1481/3780 0.200 1.126 0.31622776601683794 StandardScaler() 0 -> 0.46410003321985577
 1482/3780 0.200 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5004542246086617
 1483/3780 0.200 1.126 1.0 StandardScaler() 0 -> 0.49790549218356706
 1484/3780 0.200 1.126 1.0 MinMaxScaler()   0 -> 0.48413335151442355
 1485/3780 0.200 1.429 scale StandardScaler() 0 -> 0.4718903574391624
 1486/3780 0.200 1.429 scale MinMaxScaler()   0 -> 0.473796754528573
 1487/3780 0.200 1.429 auto StandardScaler() 0 -> 0.47189035743916197
 1488/3780 0.200 1.429 auto MinMaxScaler()   0 -> 0.5289131178594094
 1489/3780 0.200 1.429 0.01 StandardScaler() 0 -> 0.49972273545243406
 1490/3780 0.200 1.429 0.01 MinMaxScaler()   0 -> 0.564627590792852
 1491/3780 0.200 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48456365375734606
 1492/3780 0.200 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.550113740772713
 1493/3780 0.200 1.429 0.1 StandardScaler() 0 -> 0.47071571042500165
 1494/3780 0.200 1.429 0.1 MinMaxScaler()   0 -> 0.5264132549235963
 1495/3780 0.200 1.429 0.31622776601683794 StandardScaler() 0 -> 0.46457815674494857
 1496/3780 0.200 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.49879815534273786
 1497/3780 0.200 1.429 1.0 StandardScaler() 0 -> 0.4940765999807944
 1498/3780 0.200 1.429 1.0 MinMaxScaler()   0 -> 0.4830593428296712
 1499/3780 0.200 1.814 scale StandardScaler() 0 -> 0.4713196289159833
 1500/3780 0.200 1.814 scale MinMaxScaler()   0 -> 0.4733057275772599
 1501/3780 0.200 1.814 auto StandardScaler() 0 -> 0.4713196289159833
 1502/3780 0.200 1.814 auto MinMaxScaler()   0 -> 0.5258097179522315
 1503/3780 0.200 1.814 0.01 StandardScaler() 0 -> 0.4981311806172551
 1504/3780 0.200 1.814 0.01 MinMaxScaler()   0 -> 0.5612611194615855
 1505/3780 0.200 1.814 0.03162277660168379 StandardScaler() 0 -> 0.4828029688679259
 1506/3780 0.200 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5483157253105895
 1507/3780 0.200 1.814 0.1 StandardScaler() 0 -> 0.4703197804431604
 1508/3780 0.200 1.814 0.1 MinMaxScaler()   0 -> 0.5231011178520296
 1509/3780 0.200 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4656283035909485
 1510/3780 0.200 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.4973281657828388
 1511/3780 0.200 1.814 1.0 StandardScaler() 0 -> 0.4921775761996747
 1512/3780 0.200 1.814 1.0 MinMaxScaler()   0 -> 0.48245896858977283
 1513/3780 0.200 2.302 scale StandardScaler() 0 -> 0.47124308297597234
 1514/3780 0.200 2.302 scale MinMaxScaler()   0 -> 0.4731175783832373
 1515/3780 0.200 2.302 auto StandardScaler() 0 -> 0.4712430829759721
 1516/3780 0.200 2.302 auto MinMaxScaler()   0 -> 0.5224510985926818
 1517/3780 0.200 2.302 0.01 StandardScaler() 0 -> 0.4969894866297111
 1518/3780 0.200 2.302 0.01 MinMaxScaler()   0 -> 0.5583295072918645
 1519/3780 0.200 2.302 0.03162277660168379 StandardScaler() 0 -> 0.4823825669350235
 1520/3780 0.200 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5466040583599643
 1521/3780 0.200 2.302 0.1 StandardScaler() 0 -> 0.4707971174342463
 1522/3780 0.200 2.302 0.1 MinMaxScaler()   0 -> 0.5198367748680749
 1523/3780 0.200 2.302 0.31622776601683794 StandardScaler() 0 -> 0.469521345982548
 1524/3780 0.200 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.4963888505224317
 1525/3780 0.200 2.302 1.0 StandardScaler() 0 -> 0.49190608521475365
 1526/3780 0.200 2.302 1.0 MinMaxScaler()   0 -> 0.4814827366590144
 1527/3780 0.200 2.921 scale StandardScaler() 0 -> 0.4723887668112403
 1528/3780 0.200 2.921 scale MinMaxScaler()   0 -> 0.47396257760012944
 1529/3780 0.200 2.921 auto StandardScaler() 0 -> 0.4723887668112405
 1530/3780 0.200 2.921 auto MinMaxScaler()   0 -> 0.5192417609243355
 1531/3780 0.200 2.921 0.01 StandardScaler() 0 -> 0.4960348007435287
 1532/3780 0.200 2.921 0.01 MinMaxScaler()   0 -> 0.5560838998178851
 1533/3780 0.200 2.921 0.03162277660168379 StandardScaler() 0 -> 0.48157318206807737
 1534/3780 0.200 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.544971248446829
 1535/3780 0.200 2.921 0.1 StandardScaler() 0 -> 0.47279594409913955
 1536/3780 0.200 2.921 0.1 MinMaxScaler()   0 -> 0.5164375198249734
 1537/3780 0.200 2.921 0.31622776601683794 StandardScaler() 0 -> 0.4742386788197163
 1538/3780 0.200 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49566042005534583
 1539/3780 0.200 2.921 1.0 StandardScaler() 0 -> 0.4916844036429275
 1540/3780 0.200 2.921 1.0 MinMaxScaler()   0 -> 0.4805694174664987
 1541/3780 0.200 3.707 scale StandardScaler() 0 -> 0.47462620215839163
 1542/3780 0.200 3.707 scale MinMaxScaler()   0 -> 0.47502781530981303
 1543/3780 0.200 3.707 auto StandardScaler() 0 -> 0.47462620215839185
 1544/3780 0.200 3.707 auto MinMaxScaler()   0 -> 0.5158490968057771
 1545/3780 0.200 3.707 0.01 StandardScaler() 0 -> 0.49500436146166304
 1546/3780 0.200 3.707 0.01 MinMaxScaler()   0 -> 0.5545739538877905
 1547/3780 0.200 3.707 0.03162277660168379 StandardScaler() 0 -> 0.48073093382078375
 1548/3780 0.200 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5431370077739168
 1549/3780 0.200 3.707 0.1 StandardScaler() 0 -> 0.47478353353628777
 1550/3780 0.200 3.707 0.1 MinMaxScaler()   0 -> 0.5134230469835522
 1551/3780 0.200 3.707 0.31622776601683794 StandardScaler() 0 -> 0.47881923969890844
 1552/3780 0.200 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.49465391892655797
 1553/3780 0.200 3.707 1.0 StandardScaler() 0 -> 0.4917338387403521
 1554/3780 0.200 3.707 1.0 MinMaxScaler()   0 -> 0.48016412122658014
 1555/3780 0.200 4.703 scale StandardScaler() 0 -> 0.4767103814371986
 1556/3780 0.200 4.703 scale MinMaxScaler()   0 -> 0.4768087588092973
 1557/3780 0.200 4.703 auto StandardScaler() 0 -> 0.4767103814371982
 1558/3780 0.200 4.703 auto MinMaxScaler()   0 -> 0.5130082600883636
 1559/3780 0.200 4.703 0.01 StandardScaler() 0 -> 0.4941082759447288
 1560/3780 0.200 4.703 0.01 MinMaxScaler()   0 -> 0.5532443681560321
 1561/3780 0.200 4.703 0.03162277660168379 StandardScaler() 0 -> 0.47987792880226965
 1562/3780 0.200 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5407054139742226
 1563/3780 0.200 4.703 0.1 StandardScaler() 0 -> 0.47639939735018383
 1564/3780 0.200 4.703 0.1 MinMaxScaler()   0 -> 0.5108752362716562
 1565/3780 0.200 4.703 0.31622776601683794 StandardScaler() 0 -> 0.48382519646474414
 1566/3780 0.200 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.4937520610082207
 1567/3780 0.200 4.703 1.0 StandardScaler() 0 -> 0.4918255639155249
 1568/3780 0.200 4.703 1.0 MinMaxScaler()   0 -> 0.47930124873491115
 1569/3780 0.200 5.968 scale StandardScaler() 0 -> 0.47891849033386275
 1570/3780 0.200 5.968 scale MinMaxScaler()   0 -> 0.4793919564583995
 1571/3780 0.200 5.968 auto StandardScaler() 0 -> 0.4789184903338623
 1572/3780 0.200 5.968 auto MinMaxScaler()   0 -> 0.5103464115111319
 1573/3780 0.200 5.968 0.01 StandardScaler() 0 -> 0.49341410733712165
 1574/3780 0.200 5.968 0.01 MinMaxScaler()   0 -> 0.5524889758601557
 1575/3780 0.200 5.968 0.03162277660168379 StandardScaler() 0 -> 0.4794986879915158
 1576/3780 0.200 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5381537240731463
 1577/3780 0.200 5.968 0.1 StandardScaler() 0 -> 0.47879103905692916
 1578/3780 0.200 5.968 0.1 MinMaxScaler()   0 -> 0.5079934115762105
 1579/3780 0.200 5.968 0.31622776601683794 StandardScaler() 0 -> 0.4906553131674192
 1580/3780 0.200 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.49243907982573504
 1581/3780 0.200 5.968 1.0 StandardScaler() 0 -> 0.4919507452713097
 1582/3780 0.200 5.968 1.0 MinMaxScaler()   0 -> 0.4788324369596599
 1583/3780 0.200 7.574 scale StandardScaler() 0 -> 0.4816218140866928
 1584/3780 0.200 7.574 scale MinMaxScaler()   0 -> 0.48143524342775645
 1585/3780 0.200 7.574 auto StandardScaler() 0 -> 0.4816218140866935
 1586/3780 0.200 7.574 auto MinMaxScaler()   0 -> 0.5074632429148651
 1587/3780 0.200 7.574 0.01 StandardScaler() 0 -> 0.4925109934068311
 1588/3780 0.200 7.574 0.01 MinMaxScaler()   0 -> 0.5514766051071285
 1589/3780 0.200 7.574 0.03162277660168379 StandardScaler() 0 -> 0.479651474834784
 1590/3780 0.200 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5350512954461766
 1591/3780 0.200 7.574 0.1 StandardScaler() 0 -> 0.4829814921973606
 1592/3780 0.200 7.574 0.1 MinMaxScaler()   0 -> 0.5053276311548415
 1593/3780 0.200 7.574 0.31622776601683794 StandardScaler() 0 -> 0.4980437479148497
 1594/3780 0.200 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.4918361187553111
 1595/3780 0.200 7.574 1.0 StandardScaler() 0 -> 0.49197620582402246
 1596/3780 0.200 7.574 1.0 MinMaxScaler()   0 -> 0.4791054508726656
 1597/3780 0.200 9.611 scale StandardScaler() 0 -> 0.486447400477311
 1598/3780 0.200 9.611 scale MinMaxScaler()   0 -> 0.48350582299007394
 1599/3780 0.200 9.611 auto StandardScaler() 0 -> 0.48644740047731266
 1600/3780 0.200 9.611 auto MinMaxScaler()   0 -> 0.5050607309694977
 1601/3780 0.200 9.611 0.01 StandardScaler() 0 -> 0.49151034422161577
 1602/3780 0.200 9.611 0.01 MinMaxScaler()   0 -> 0.5507709898886933
 1603/3780 0.200 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4801592118427511
 1604/3780 0.200 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5318484116453188
 1605/3780 0.200 9.611 0.1 StandardScaler() 0 -> 0.48939988926398215
 1606/3780 0.200 9.611 0.1 MinMaxScaler()   0 -> 0.5029493542103737
 1607/3780 0.200 9.611 0.31622776601683794 StandardScaler() 0 -> 0.5039505358217008
 1608/3780 0.200 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4910237635352037
 1609/3780 0.200 9.611 1.0 StandardScaler() 0 -> 0.49198378212432076
 1610/3780 0.200 9.611 1.0 MinMaxScaler()   0 -> 0.4795931894732459
 1611/3780 0.200 12.196 scale StandardScaler() 0 -> 0.49337295804347664
 1612/3780 0.200 12.196 scale MinMaxScaler()   0 -> 0.48682236824858854
 1613/3780 0.200 12.196 auto StandardScaler() 0 -> 0.493372958043476
 1614/3780 0.200 12.196 auto MinMaxScaler()   0 -> 0.502674848567812
 1615/3780 0.200 12.196 0.01 StandardScaler() 0 -> 0.4915715188861522
 1616/3780 0.200 12.196 0.01 MinMaxScaler()   0 -> 0.5499732983266719
 1617/3780 0.200 12.196 0.03162277660168379 StandardScaler() 0 -> 0.48083975488065717
 1618/3780 0.200 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5284022053675163
 1619/3780 0.200 12.196 0.1 StandardScaler() 0 -> 0.5002867810191375
 1620/3780 0.200 12.196 0.1 MinMaxScaler()   0 -> 0.501579805829408
 1621/3780 0.200 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5084278875760069
 1622/3780 0.200 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4906517889478377
 1623/3780 0.200 12.196 1.0 StandardScaler() 0 -> 0.49198140967204385
 1624/3780 0.200 12.196 1.0 MinMaxScaler()   0 -> 0.4801058669880513
 1625/3780 0.200 15.476 scale StandardScaler() 0 -> 0.5041817934338447
 1626/3780 0.200 15.476 scale MinMaxScaler()   0 -> 0.49178799755574926
 1627/3780 0.200 15.476 auto StandardScaler() 0 -> 0.5041817934338434
 1628/3780 0.200 15.476 auto MinMaxScaler()   0 -> 0.5012713856535783
 1629/3780 0.200 15.476 0.01 StandardScaler() 0 -> 0.4904967700410389
 1630/3780 0.200 15.476 0.01 MinMaxScaler()   0 -> 0.5490545460973776
 1631/3780 0.200 15.476 0.03162277660168379 StandardScaler() 0 -> 0.48110698678786173
 1632/3780 0.200 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5250417918041042
 1633/3780 0.200 15.476 0.1 StandardScaler() 0 -> 0.5162884306967306
 1634/3780 0.200 15.476 0.1 MinMaxScaler()   0 -> 0.49992937534843257
 1635/3780 0.200 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5115075891406833
 1636/3780 0.200 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.48983451228062885
 1637/3780 0.200 15.476 1.0 StandardScaler() 0 -> 0.49198140967204385
 1638/3780 0.200 15.476 1.0 MinMaxScaler()   0 -> 0.48087010929560753
 1639/3780 0.200 19.638 scale StandardScaler() 0 -> 0.5209201225855421
 1640/3780 0.200 19.638 scale MinMaxScaler()   0 -> 0.4991451856078706
 1641/3780 0.200 19.638 auto StandardScaler() 0 -> 0.5209201225855381
 1642/3780 0.200 19.638 auto MinMaxScaler()   0 -> 0.4997541437738476
 1643/3780 0.200 19.638 0.01 StandardScaler() 0 -> 0.49013322948235166
 1644/3780 0.200 19.638 0.01 MinMaxScaler()   0 -> 0.5475906212101203
 1645/3780 0.200 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4821002762447794
 1646/3780 0.200 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5218333195752624
 1647/3780 0.200 19.638 0.1 StandardScaler() 0 -> 0.5359570152035781
 1648/3780 0.200 19.638 0.1 MinMaxScaler()   0 -> 0.49830544225461454
 1649/3780 0.200 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5146908453411106
 1650/3780 0.200 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.4895599323236288
 1651/3780 0.200 19.638 1.0 StandardScaler() 0 -> 0.49198140967204385
 1652/3780 0.200 19.638 1.0 MinMaxScaler()   0 -> 0.48172006769909875
 1653/3780 0.200 24.920 scale StandardScaler() 0 -> 0.541311139125594
 1654/3780 0.200 24.920 scale MinMaxScaler()   0 -> 0.5103285178849567
 1655/3780 0.200 24.920 auto StandardScaler() 0 -> 0.541311139125597
 1656/3780 0.200 24.920 auto MinMaxScaler()   0 -> 0.4983188659307447
 1657/3780 0.200 24.920 0.01 StandardScaler() 0 -> 0.48946638361716777
 1658/3780 0.200 24.920 0.01 MinMaxScaler()   0 -> 0.5461141116066196
 1659/3780 0.200 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4836947696941896
 1660/3780 0.200 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5188452996138285
 1661/3780 0.200 24.920 0.1 StandardScaler() 0 -> 0.5574396846858621
 1662/3780 0.200 24.920 0.1 MinMaxScaler()   0 -> 0.498258731026792
 1663/3780 0.200 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5176894143956036
 1664/3780 0.200 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.4884666712592103
 1665/3780 0.200 24.920 1.0 StandardScaler() 0 -> 0.49198140967204385
 1666/3780 0.200 24.920 1.0 MinMaxScaler()   0 -> 0.48360931705822807
 1667/3780 0.200 31.623 scale StandardScaler() 0 -> 0.5643038312680864
 1668/3780 0.200 31.623 scale MinMaxScaler()   0 -> 0.5251421862536044
 1669/3780 0.200 31.623 auto StandardScaler() 0 -> 0.5643038312680785
 1670/3780 0.200 31.623 auto MinMaxScaler()   0 -> 0.49844999182467226
 1671/3780 0.200 31.623 0.01 StandardScaler() 0 -> 0.4887048670615896
 1672/3780 0.200 31.623 0.01 MinMaxScaler()   0 -> 0.544276906772535
 1673/3780 0.200 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4856190985555971
 1674/3780 0.200 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5156931803369207
 1675/3780 0.200 31.623 0.1 StandardScaler() 0 -> 0.5803625465747068
 1676/3780 0.200 31.623 0.1 MinMaxScaler()   0 -> 0.49779892743557524
 1677/3780 0.200 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5192438557417538
 1678/3780 0.200 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4880067610750574
 1679/3780 0.200 31.623 1.0 StandardScaler() 0 -> 0.49198140967204385
 1680/3780 0.200 31.623 1.0 MinMaxScaler()   0 -> 0.48609871987488074
 1681/3780 0.250 0.032 scale StandardScaler() 0 -> 0.5285852960741085
 1682/3780 0.250 0.032 scale MinMaxScaler()   0 -> 0.5235075056756723
 1683/3780 0.250 0.032 auto StandardScaler() 0 -> 0.5285852960741085
 1684/3780 0.250 0.032 auto MinMaxScaler()   0 -> 0.632120758303752
 1685/3780 0.250 0.032 0.01 StandardScaler() 0 -> 0.5823462042143855
 1686/3780 0.250 0.032 0.01 MinMaxScaler()   0 -> 0.7561563462913933
 1687/3780 0.250 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5383260142605478
 1688/3780 0.250 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.707569672294268
 1689/3780 0.250 0.032 0.1 StandardScaler() 0 -> 0.5302901216201005
 1690/3780 0.250 0.032 0.1 MinMaxScaler()   0 -> 0.626697488638423
 1691/3780 0.250 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6030631051893737
 1692/3780 0.250 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5709238534984462
 1693/3780 0.250 0.032 1.0 StandardScaler() 0 -> 0.7513953139732852
 1694/3780 0.250 0.032 1.0 MinMaxScaler()   0 -> 0.5314772250008338
 1695/3780 0.250 0.040 scale StandardScaler() 0 -> 0.5207726208131195
 1696/3780 0.250 0.040 scale MinMaxScaler()   0 -> 0.51682874116449
 1697/3780 0.250 0.040 auto StandardScaler() 0 -> 0.5207726208131195
 1698/3780 0.250 0.040 auto MinMaxScaler()   0 -> 0.6183274762319703
 1699/3780 0.250 0.040 0.01 StandardScaler() 0 -> 0.5726479960709879
 1700/3780 0.250 0.040 0.01 MinMaxScaler()   0 -> 0.7494312768071629
 1701/3780 0.250 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5313315155275606
 1702/3780 0.250 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.6911045166308217
 1703/3780 0.250 0.040 0.1 StandardScaler() 0 -> 0.5217150216337495
 1704/3780 0.250 0.040 0.1 MinMaxScaler()   0 -> 0.613772722517353
 1705/3780 0.250 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5860145742632042
 1706/3780 0.250 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5612985205326231
 1707/3780 0.250 0.040 1.0 StandardScaler() 0 -> 0.7435134430901901
 1708/3780 0.250 0.040 1.0 MinMaxScaler()   0 -> 0.5254475105371266
 1709/3780 0.250 0.051 scale StandardScaler() 0 -> 0.5143041044315896
 1710/3780 0.250 0.051 scale MinMaxScaler()   0 -> 0.5115124608810201
 1711/3780 0.250 0.051 auto StandardScaler() 0 -> 0.5143041044315896
 1712/3780 0.250 0.051 auto MinMaxScaler()   0 -> 0.6063808458274978
 1713/3780 0.250 0.051 0.01 StandardScaler() 0 -> 0.5625763665303544
 1714/3780 0.250 0.051 0.01 MinMaxScaler()   0 -> 0.7411362691315895
 1715/3780 0.250 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5247777733763822
 1716/3780 0.250 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6724338395063447
 1717/3780 0.250 0.051 0.1 StandardScaler() 0 -> 0.5152008395402433
 1718/3780 0.250 0.051 0.1 MinMaxScaler()   0 -> 0.601167897793961
 1719/3780 0.250 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5699758277241757
 1720/3780 0.250 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5529737834571185
 1721/3780 0.250 0.051 1.0 StandardScaler() 0 -> 0.733822953255136
 1722/3780 0.250 0.051 1.0 MinMaxScaler()   0 -> 0.5199975518096963
 1723/3780 0.250 0.065 scale StandardScaler() 0 -> 0.5081787647490982
 1724/3780 0.250 0.065 scale MinMaxScaler()   0 -> 0.5066885765816443
 1725/3780 0.250 0.065 auto StandardScaler() 0 -> 0.5081787647490982
 1726/3780 0.250 0.065 auto MinMaxScaler()   0 -> 0.5931594178843156
 1727/3780 0.250 0.065 0.01 StandardScaler() 0 -> 0.5546433280200271
 1728/3780 0.250 0.065 0.01 MinMaxScaler()   0 -> 0.7309260453640203
 1729/3780 0.250 0.065 0.03162277660168379 StandardScaler() 0 -> 0.519111417274721
 1730/3780 0.250 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6528992097648694
 1731/3780 0.250 0.065 0.1 StandardScaler() 0 -> 0.5086703276136127
 1732/3780 0.250 0.065 0.1 MinMaxScaler()   0 -> 0.589114346599685
 1733/3780 0.250 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5564424694143045
 1734/3780 0.250 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5455091122293246
 1735/3780 0.250 0.065 1.0 StandardScaler() 0 -> 0.7220283116705498
 1736/3780 0.250 0.065 1.0 MinMaxScaler()   0 -> 0.5155752160199663
 1737/3780 0.250 0.082 scale StandardScaler() 0 -> 0.5024150393292469
 1738/3780 0.250 0.082 scale MinMaxScaler()   0 -> 0.5023056901022536
 1739/3780 0.250 0.082 auto StandardScaler() 0 -> 0.5024150393292469
 1740/3780 0.250 0.082 auto MinMaxScaler()   0 -> 0.581860290670109
 1741/3780 0.250 0.082 0.01 StandardScaler() 0 -> 0.5466119533914543
 1742/3780 0.250 0.082 0.01 MinMaxScaler()   0 -> 0.718626790485712
 1743/3780 0.250 0.082 0.03162277660168379 StandardScaler() 0 -> 0.513884131842647
 1744/3780 0.250 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.636139656021437
 1745/3780 0.250 0.082 0.1 StandardScaler() 0 -> 0.503210231903764
 1746/3780 0.250 0.082 0.1 MinMaxScaler()   0 -> 0.5777623760619801
 1747/3780 0.250 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5430851285624958
 1748/3780 0.250 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5404460085682979
 1749/3780 0.250 0.082 1.0 StandardScaler() 0 -> 0.7079799495676494
 1750/3780 0.250 0.082 1.0 MinMaxScaler()   0 -> 0.5109240430247267
 1751/3780 0.250 0.104 scale StandardScaler() 0 -> 0.4973529404117389
 1752/3780 0.250 0.104 scale MinMaxScaler()   0 -> 0.49759435039173927
 1753/3780 0.250 0.104 auto StandardScaler() 0 -> 0.4973529404117389
 1754/3780 0.250 0.104 auto MinMaxScaler()   0 -> 0.5728231911445666
 1755/3780 0.250 0.104 0.01 StandardScaler() 0 -> 0.5409403359218936
 1756/3780 0.250 0.104 0.01 MinMaxScaler()   0 -> 0.703995175224438
 1757/3780 0.250 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5089938211984227
 1758/3780 0.250 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6223454817432735
 1759/3780 0.250 0.104 0.1 StandardScaler() 0 -> 0.49747096382774925
 1760/3780 0.250 0.104 0.1 MinMaxScaler()   0 -> 0.5690156726398916
 1761/3780 0.250 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5307154820755579
 1762/3780 0.250 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5360863279896888
 1763/3780 0.250 0.104 1.0 StandardScaler() 0 -> 0.6915113188388653
 1764/3780 0.250 0.104 1.0 MinMaxScaler()   0 -> 0.5070367972840165
 1765/3780 0.250 0.132 scale StandardScaler() 0 -> 0.4922824626999677
 1766/3780 0.250 0.132 scale MinMaxScaler()   0 -> 0.4936249562153052
 1767/3780 0.250 0.132 auto StandardScaler() 0 -> 0.49228246269996756
 1768/3780 0.250 0.132 auto MinMaxScaler()   0 -> 0.5647551438184865
 1769/3780 0.250 0.132 0.01 StandardScaler() 0 -> 0.5363302767701683
 1770/3780 0.250 0.132 0.01 MinMaxScaler()   0 -> 0.6869870290572351
 1771/3780 0.250 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5046148834004961
 1772/3780 0.250 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6106844367756158
 1773/3780 0.250 0.132 0.1 StandardScaler() 0 -> 0.4923560347416127
 1774/3780 0.250 0.132 0.1 MinMaxScaler()   0 -> 0.5617509796971228
 1775/3780 0.250 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5186402072971289
 1776/3780 0.250 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5314671304634414
 1777/3780 0.250 0.132 1.0 StandardScaler() 0 -> 0.6726859489237965
 1778/3780 0.250 0.132 1.0 MinMaxScaler()   0 -> 0.5033966056313991
 1779/3780 0.250 0.168 scale StandardScaler() 0 -> 0.48810758601984894
 1780/3780 0.250 0.168 scale MinMaxScaler()   0 -> 0.48970144447623815
 1781/3780 0.250 0.168 auto StandardScaler() 0 -> 0.488107586019849
 1782/3780 0.250 0.168 auto MinMaxScaler()   0 -> 0.558999483116792
 1783/3780 0.250 0.168 0.01 StandardScaler() 0 -> 0.5318841653116722
 1784/3780 0.250 0.168 0.01 MinMaxScaler()   0 -> 0.6680816151848421
 1785/3780 0.250 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5008105245382035
 1786/3780 0.250 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.5973709352102353
 1787/3780 0.250 0.168 0.1 StandardScaler() 0 -> 0.48791262497865745
 1788/3780 0.250 0.168 0.1 MinMaxScaler()   0 -> 0.556248635205569
 1789/3780 0.250 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5083541857036581
 1790/3780 0.250 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5272442703557824
 1791/3780 0.250 0.168 1.0 StandardScaler() 0 -> 0.6519520669151443
 1792/3780 0.250 0.168 1.0 MinMaxScaler()   0 -> 0.4998527456284973
 1793/3780 0.250 0.213 scale StandardScaler() 0 -> 0.48518634731298577
 1794/3780 0.250 0.213 scale MinMaxScaler()   0 -> 0.4866818470499957
 1795/3780 0.250 0.213 auto StandardScaler() 0 -> 0.4851863473129858
 1796/3780 0.250 0.213 auto MinMaxScaler()   0 -> 0.5536469005230967
 1797/3780 0.250 0.213 0.01 StandardScaler() 0 -> 0.5274878907055598
 1798/3780 0.250 0.213 0.01 MinMaxScaler()   0 -> 0.6488685907903226
 1799/3780 0.250 0.213 0.03162277660168379 StandardScaler() 0 -> 0.497966043379601
 1800/3780 0.250 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5863395537332997
 1801/3780 0.250 0.213 0.1 StandardScaler() 0 -> 0.48475338954120256
 1802/3780 0.250 0.213 0.1 MinMaxScaler()   0 -> 0.5512878853327511
 1803/3780 0.250 0.213 0.31622776601683794 StandardScaler() 0 -> 0.4987961470620945
 1804/3780 0.250 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5234631428090176
 1805/3780 0.250 0.213 1.0 StandardScaler() 0 -> 0.6291004441708085
 1806/3780 0.250 0.213 1.0 MinMaxScaler()   0 -> 0.4968435916395364
 1807/3780 0.250 0.270 scale StandardScaler() 0 -> 0.4827099277388891
 1808/3780 0.250 0.270 scale MinMaxScaler()   0 -> 0.48445720547051935
 1809/3780 0.250 0.270 auto StandardScaler() 0 -> 0.4827099277388891
 1810/3780 0.250 0.270 auto MinMaxScaler()   0 -> 0.5500954210867063
 1811/3780 0.250 0.270 0.01 StandardScaler() 0 -> 0.5221810518067495
 1812/3780 0.250 0.270 0.01 MinMaxScaler()   0 -> 0.6331405183801787
 1813/3780 0.250 0.270 0.03162277660168379 StandardScaler() 0 -> 0.4956479876713333
 1814/3780 0.250 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.576548379798337
 1815/3780 0.250 0.270 0.1 StandardScaler() 0 -> 0.482065728723929
 1816/3780 0.250 0.270 0.1 MinMaxScaler()   0 -> 0.5483451983413166
 1817/3780 0.250 0.270 0.31622776601683794 StandardScaler() 0 -> 0.48999846384639484
 1818/3780 0.250 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5197585920400695
 1819/3780 0.250 0.270 1.0 StandardScaler() 0 -> 0.6049355724695094
 1820/3780 0.250 0.270 1.0 MinMaxScaler()   0 -> 0.4944085103560231
 1821/3780 0.250 0.342 scale StandardScaler() 0 -> 0.4806280347557917
 1822/3780 0.250 0.342 scale MinMaxScaler()   0 -> 0.482467976237915
 1823/3780 0.250 0.342 auto StandardScaler() 0 -> 0.48062803475579186
 1824/3780 0.250 0.342 auto MinMaxScaler()   0 -> 0.5474503313718784
 1825/3780 0.250 0.342 0.01 StandardScaler() 0 -> 0.5184177393943784
 1826/3780 0.250 0.342 0.01 MinMaxScaler()   0 -> 0.6195447564768176
 1827/3780 0.250 0.342 0.03162277660168379 StandardScaler() 0 -> 0.49291696022095716
 1828/3780 0.250 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5691315439966781
 1829/3780 0.250 0.342 0.1 StandardScaler() 0 -> 0.4799403727503164
 1830/3780 0.250 0.342 0.1 MinMaxScaler()   0 -> 0.5455867897894006
 1831/3780 0.250 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4826329119460158
 1832/3780 0.250 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5162104170118224
 1833/3780 0.250 0.342 1.0 StandardScaler() 0 -> 0.5812245845069467
 1834/3780 0.250 0.342 1.0 MinMaxScaler()   0 -> 0.4923048214060381
 1835/3780 0.250 0.434 scale StandardScaler() 0 -> 0.47824773185000574
 1836/3780 0.250 0.434 scale MinMaxScaler()   0 -> 0.47992883522659663
 1837/3780 0.250 0.434 auto StandardScaler() 0 -> 0.4782477318500058
 1838/3780 0.250 0.434 auto MinMaxScaler()   0 -> 0.5445468429638094
 1839/3780 0.250 0.434 0.01 StandardScaler() 0 -> 0.5153128948126471
 1840/3780 0.250 0.434 0.01 MinMaxScaler()   0 -> 0.6078203343027545
 1841/3780 0.250 0.434 0.03162277660168379 StandardScaler() 0 -> 0.4914610332195601
 1842/3780 0.250 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5632434396029135
 1843/3780 0.250 0.434 0.1 StandardScaler() 0 -> 0.47755045552948716
 1844/3780 0.250 0.434 0.1 MinMaxScaler()   0 -> 0.5427900851792693
 1845/3780 0.250 0.434 0.31622776601683794 StandardScaler() 0 -> 0.4765819470928719
 1846/3780 0.250 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5132790241374116
 1847/3780 0.250 0.434 1.0 StandardScaler() 0 -> 0.5590298853620815
 1848/3780 0.250 0.434 1.0 MinMaxScaler()   0 -> 0.4904677359471543
 1849/3780 0.250 0.551 scale StandardScaler() 0 -> 0.47637459873926913
 1850/3780 0.250 0.551 scale MinMaxScaler()   0 -> 0.477173860615522
 1851/3780 0.250 0.551 auto StandardScaler() 0 -> 0.4763745987392692
 1852/3780 0.250 0.551 auto MinMaxScaler()   0 -> 0.5422473620567266
 1853/3780 0.250 0.551 0.01 StandardScaler() 0 -> 0.511760537017062
 1854/3780 0.250 0.551 0.01 MinMaxScaler()   0 -> 0.5950177249912514
 1855/3780 0.250 0.551 0.03162277660168379 StandardScaler() 0 -> 0.48906854170778197
 1856/3780 0.250 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5583667260105135
 1857/3780 0.250 0.551 0.1 StandardScaler() 0 -> 0.47593705895367466
 1858/3780 0.250 0.551 0.1 MinMaxScaler()   0 -> 0.5400123906494735
 1859/3780 0.250 0.551 0.31622776601683794 StandardScaler() 0 -> 0.47137822430190496
 1860/3780 0.250 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5096883495765807
 1861/3780 0.250 0.551 1.0 StandardScaler() 0 -> 0.5371834290155556
 1862/3780 0.250 0.551 1.0 MinMaxScaler()   0 -> 0.48833426832490323
 1863/3780 0.250 0.700 scale StandardScaler() 0 -> 0.4752064913213565
 1864/3780 0.250 0.700 scale MinMaxScaler()   0 -> 0.4747214170792602
 1865/3780 0.250 0.700 auto StandardScaler() 0 -> 0.47520649132135634
 1866/3780 0.250 0.700 auto MinMaxScaler()   0 -> 0.5394126668403875
 1867/3780 0.250 0.700 0.01 StandardScaler() 0 -> 0.5087915424249146
 1868/3780 0.250 0.700 0.01 MinMaxScaler()   0 -> 0.5841756939064466
 1869/3780 0.250 0.700 0.03162277660168379 StandardScaler() 0 -> 0.486817555722034
 1870/3780 0.250 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5550029868418646
 1871/3780 0.250 0.700 0.1 StandardScaler() 0 -> 0.4743800143060832
 1872/3780 0.250 0.700 0.1 MinMaxScaler()   0 -> 0.5368742096677978
 1873/3780 0.250 0.700 0.31622776601683794 StandardScaler() 0 -> 0.46757233885497484
 1874/3780 0.250 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5066701599506716
 1875/3780 0.250 0.700 1.0 StandardScaler() 0 -> 0.5186410847224717
 1876/3780 0.250 0.700 1.0 MinMaxScaler()   0 -> 0.48631612897371407
 1877/3780 0.250 0.888 scale StandardScaler() 0 -> 0.47416342401585004
 1878/3780 0.250 0.888 scale MinMaxScaler()   0 -> 0.4733628264166672
 1879/3780 0.250 0.888 auto StandardScaler() 0 -> 0.47416342401585015
 1880/3780 0.250 0.888 auto MinMaxScaler()   0 -> 0.5362564252972875
 1881/3780 0.250 0.888 0.01 StandardScaler() 0 -> 0.5054415315214488
 1882/3780 0.250 0.888 0.01 MinMaxScaler()   0 -> 0.575983934600926
 1883/3780 0.250 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4853518095507679
 1884/3780 0.250 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5533070803049316
 1885/3780 0.250 0.888 0.1 StandardScaler() 0 -> 0.47225620624795656
 1886/3780 0.250 0.888 0.1 MinMaxScaler()   0 -> 0.5337344940888825
 1887/3780 0.250 0.888 0.31622776601683794 StandardScaler() 0 -> 0.46504117294754516
 1888/3780 0.250 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5036211673267047
 1889/3780 0.250 0.888 1.0 StandardScaler() 0 -> 0.5077659947257833
 1890/3780 0.250 0.888 1.0 MinMaxScaler()   0 -> 0.4849703949299249
 1891/3780 0.250 1.126 scale StandardScaler() 0 -> 0.4720434685616495
 1892/3780 0.250 1.126 scale MinMaxScaler()   0 -> 0.4727416553531945
 1893/3780 0.250 1.126 auto StandardScaler() 0 -> 0.4720434685616495
 1894/3780 0.250 1.126 auto MinMaxScaler()   0 -> 0.5332435656030085
 1895/3780 0.250 1.126 0.01 StandardScaler() 0 -> 0.5023379674512096
 1896/3780 0.250 1.126 0.01 MinMaxScaler()   0 -> 0.5685680292157543
 1897/3780 0.250 1.126 0.03162277660168379 StandardScaler() 0 -> 0.4844577115538488
 1898/3780 0.250 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5516264096533737
 1899/3780 0.250 1.126 0.1 StandardScaler() 0 -> 0.4705455404071599
 1900/3780 0.250 1.126 0.1 MinMaxScaler()   0 -> 0.5308465353660403
 1901/3780 0.250 1.126 0.31622776601683794 StandardScaler() 0 -> 0.46400899687570085
 1902/3780 0.250 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5015381917959884
 1903/3780 0.250 1.126 1.0 StandardScaler() 0 -> 0.5025910537535976
 1904/3780 0.250 1.126 1.0 MinMaxScaler()   0 -> 0.4838818388891943
 1905/3780 0.250 1.429 scale StandardScaler() 0 -> 0.47111821782560176
 1906/3780 0.250 1.429 scale MinMaxScaler()   0 -> 0.47227070251633935
 1907/3780 0.250 1.429 auto StandardScaler() 0 -> 0.47111821782560126
 1908/3780 0.250 1.429 auto MinMaxScaler()   0 -> 0.5301777924845416
 1909/3780 0.250 1.429 0.01 StandardScaler() 0 -> 0.5002169029772477
 1910/3780 0.250 1.429 0.01 MinMaxScaler()   0 -> 0.5630028484149653
 1911/3780 0.250 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48387540906671456
 1912/3780 0.250 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5501612771504601
 1913/3780 0.250 1.429 0.1 StandardScaler() 0 -> 0.47001964550940367
 1914/3780 0.250 1.429 0.1 MinMaxScaler()   0 -> 0.5279636376815268
 1915/3780 0.250 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4635654466182942
 1916/3780 0.250 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.4989477865842251
 1917/3780 0.250 1.429 1.0 StandardScaler() 0 -> 0.4985813081024315
 1918/3780 0.250 1.429 1.0 MinMaxScaler()   0 -> 0.48243560557680754
 1919/3780 0.250 1.814 scale StandardScaler() 0 -> 0.4708124845530135
 1920/3780 0.250 1.814 scale MinMaxScaler()   0 -> 0.47229255906733064
 1921/3780 0.250 1.814 auto StandardScaler() 0 -> 0.47081248455301344
 1922/3780 0.250 1.814 auto MinMaxScaler()   0 -> 0.527442623293683
 1923/3780 0.250 1.814 0.01 StandardScaler() 0 -> 0.49835090451549596
 1924/3780 0.250 1.814 0.01 MinMaxScaler()   0 -> 0.5590252099670526
 1925/3780 0.250 1.814 0.03162277660168379 StandardScaler() 0 -> 0.4825970818379044
 1926/3780 0.250 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5484345539305088
 1927/3780 0.250 1.814 0.1 StandardScaler() 0 -> 0.4696638564160624
 1928/3780 0.250 1.814 0.1 MinMaxScaler()   0 -> 0.5245896533972175
 1929/3780 0.250 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4650952622276381
 1930/3780 0.250 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.4972322657594492
 1931/3780 0.250 1.814 1.0 StandardScaler() 0 -> 0.4967144883912608
 1932/3780 0.250 1.814 1.0 MinMaxScaler()   0 -> 0.48117317658003494
 1933/3780 0.250 2.302 scale StandardScaler() 0 -> 0.47045739324501074
 1934/3780 0.250 2.302 scale MinMaxScaler()   0 -> 0.47272537890769023
 1935/3780 0.250 2.302 auto StandardScaler() 0 -> 0.47045739324501074
 1936/3780 0.250 2.302 auto MinMaxScaler()   0 -> 0.5239345844016627
 1937/3780 0.250 2.302 0.01 StandardScaler() 0 -> 0.49689266329264486
 1938/3780 0.250 2.302 0.01 MinMaxScaler()   0 -> 0.5564520783400042
 1939/3780 0.250 2.302 0.03162277660168379 StandardScaler() 0 -> 0.481463209295742
 1940/3780 0.250 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5468103601832182
 1941/3780 0.250 2.302 0.1 StandardScaler() 0 -> 0.46994858053827976
 1942/3780 0.250 2.302 0.1 MinMaxScaler()   0 -> 0.5214154294596992
 1943/3780 0.250 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4689837492723834
 1944/3780 0.250 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.496157317282401
 1945/3780 0.250 2.302 1.0 StandardScaler() 0 -> 0.49638537863718585
 1946/3780 0.250 2.302 1.0 MinMaxScaler()   0 -> 0.47986206129306136
 1947/3780 0.250 2.921 scale StandardScaler() 0 -> 0.4709994780733932
 1948/3780 0.250 2.921 scale MinMaxScaler()   0 -> 0.47293434196373757
 1949/3780 0.250 2.921 auto StandardScaler() 0 -> 0.47099947807339415
 1950/3780 0.250 2.921 auto MinMaxScaler()   0 -> 0.5206892583949312
 1951/3780 0.250 2.921 0.01 StandardScaler() 0 -> 0.49529153587351443
 1952/3780 0.250 2.921 0.01 MinMaxScaler()   0 -> 0.555124744462042
 1953/3780 0.250 2.921 0.03162277660168379 StandardScaler() 0 -> 0.4805461008009299
 1954/3780 0.250 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5448353298511917
 1955/3780 0.250 2.921 0.1 StandardScaler() 0 -> 0.47039783206291946
 1956/3780 0.250 2.921 0.1 MinMaxScaler()   0 -> 0.5183639793434919
 1957/3780 0.250 2.921 0.31622776601683794 StandardScaler() 0 -> 0.47256864355341577
 1958/3780 0.250 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49482791316995867
 1959/3780 0.250 2.921 1.0 StandardScaler() 0 -> 0.49619804642319804
 1960/3780 0.250 2.921 1.0 MinMaxScaler()   0 -> 0.47865456005096124
 1961/3780 0.250 3.707 scale StandardScaler() 0 -> 0.47178777202366745
 1962/3780 0.250 3.707 scale MinMaxScaler()   0 -> 0.4729584572288963
 1963/3780 0.250 3.707 auto StandardScaler() 0 -> 0.4717877720236676
 1964/3780 0.250 3.707 auto MinMaxScaler()   0 -> 0.517611822856471
 1965/3780 0.250 3.707 0.01 StandardScaler() 0 -> 0.49456438419372883
 1966/3780 0.250 3.707 0.01 MinMaxScaler()   0 -> 0.55369702229866
 1967/3780 0.250 3.707 0.03162277660168379 StandardScaler() 0 -> 0.48023006990057765
 1968/3780 0.250 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5431659877588685
 1969/3780 0.250 3.707 0.1 StandardScaler() 0 -> 0.47140030850913367
 1970/3780 0.250 3.707 0.1 MinMaxScaler()   0 -> 0.51460735488529
 1971/3780 0.250 3.707 0.31622776601683794 StandardScaler() 0 -> 0.4770654943536672
 1972/3780 0.250 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.49386051524034075
 1973/3780 0.250 3.707 1.0 StandardScaler() 0 -> 0.49624662056915597
 1974/3780 0.250 3.707 1.0 MinMaxScaler()   0 -> 0.47794867469717534
 1975/3780 0.250 4.703 scale StandardScaler() 0 -> 0.47325138793346033
 1976/3780 0.250 4.703 scale MinMaxScaler()   0 -> 0.47486430783170297
 1977/3780 0.250 4.703 auto StandardScaler() 0 -> 0.4732513879334612
 1978/3780 0.250 4.703 auto MinMaxScaler()   0 -> 0.514069304506506
 1979/3780 0.250 4.703 0.01 StandardScaler() 0 -> 0.4931019238190289
 1980/3780 0.250 4.703 0.01 MinMaxScaler()   0 -> 0.5529329044732303
 1981/3780 0.250 4.703 0.03162277660168379 StandardScaler() 0 -> 0.47975335817321135
 1982/3780 0.250 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5407951550636939
 1983/3780 0.250 4.703 0.1 StandardScaler() 0 -> 0.47392953923380904
 1984/3780 0.250 4.703 0.1 MinMaxScaler()   0 -> 0.5118367093293084
 1985/3780 0.250 4.703 0.31622776601683794 StandardScaler() 0 -> 0.4819173801329452
 1986/3780 0.250 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.4929275949566894
 1987/3780 0.250 4.703 1.0 StandardScaler() 0 -> 0.4963062318738006
 1988/3780 0.250 4.703 1.0 MinMaxScaler()   0 -> 0.4779695964180493
 1989/3780 0.250 5.968 scale StandardScaler() 0 -> 0.47591681918281176
 1990/3780 0.250 5.968 scale MinMaxScaler()   0 -> 0.47705668357185943
 1991/3780 0.250 5.968 auto StandardScaler() 0 -> 0.47591681918281203
 1992/3780 0.250 5.968 auto MinMaxScaler()   0 -> 0.51120786083092
 1993/3780 0.250 5.968 0.01 StandardScaler() 0 -> 0.4923888331573904
 1994/3780 0.250 5.968 0.01 MinMaxScaler()   0 -> 0.5520033929591724
 1995/3780 0.250 5.968 0.03162277660168379 StandardScaler() 0 -> 0.47901008066068745
 1996/3780 0.250 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5383074594359784
 1997/3780 0.250 5.968 0.1 StandardScaler() 0 -> 0.4766289389516845
 1998/3780 0.250 5.968 0.1 MinMaxScaler()   0 -> 0.5084168795137295
 1999/3780 0.250 5.968 0.31622776601683794 StandardScaler() 0 -> 0.4886389839358352
 2000/3780 0.250 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.4918310516902708
 2001/3780 0.250 5.968 1.0 StandardScaler() 0 -> 0.4963300894266975
 2002/3780 0.250 5.968 1.0 MinMaxScaler()   0 -> 0.4771597124465788
 2003/3780 0.250 7.574 scale StandardScaler() 0 -> 0.4789968200828629
 2004/3780 0.250 7.574 scale MinMaxScaler()   0 -> 0.47880522981641693
 2005/3780 0.250 7.574 auto StandardScaler() 0 -> 0.47899682008286276
 2006/3780 0.250 7.574 auto MinMaxScaler()   0 -> 0.5079982579788548
 2007/3780 0.250 7.574 0.01 StandardScaler() 0 -> 0.4918447778699715
 2008/3780 0.250 7.574 0.01 MinMaxScaler()   0 -> 0.5513626711852856
 2009/3780 0.250 7.574 0.03162277660168379 StandardScaler() 0 -> 0.4781812681911301
 2010/3780 0.250 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5354546367729957
 2011/3780 0.250 7.574 0.1 StandardScaler() 0 -> 0.481144382530998
 2012/3780 0.250 7.574 0.1 MinMaxScaler()   0 -> 0.5057708471883966
 2013/3780 0.250 7.574 0.31622776601683794 StandardScaler() 0 -> 0.4941428773625877
 2014/3780 0.250 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.4915448175193518
 2015/3780 0.250 7.574 1.0 StandardScaler() 0 -> 0.4963359096227918
 2016/3780 0.250 7.574 1.0 MinMaxScaler()   0 -> 0.4770249919211455
 2017/3780 0.250 9.611 scale StandardScaler() 0 -> 0.48411878727801616
 2018/3780 0.250 9.611 scale MinMaxScaler()   0 -> 0.48157854347381096
 2019/3780 0.250 9.611 auto StandardScaler() 0 -> 0.4841187872780182
 2020/3780 0.250 9.611 auto MinMaxScaler()   0 -> 0.505396773512173
 2021/3780 0.250 9.611 0.01 StandardScaler() 0 -> 0.49128579245023296
 2022/3780 0.250 9.611 0.01 MinMaxScaler()   0 -> 0.5503703404530208
 2023/3780 0.250 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4786554739284891
 2024/3780 0.250 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5326833140907236
 2025/3780 0.250 9.611 0.1 StandardScaler() 0 -> 0.4876238651032539
 2026/3780 0.250 9.611 0.1 MinMaxScaler()   0 -> 0.5035361025836298
 2027/3780 0.250 9.611 0.31622776601683794 StandardScaler() 0 -> 0.4991760283044573
 2028/3780 0.250 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.49047928013195824
 2029/3780 0.250 9.611 1.0 StandardScaler() 0 -> 0.496335060606013
 2030/3780 0.250 9.611 1.0 MinMaxScaler()   0 -> 0.4773332392168865
 2031/3780 0.250 12.196 scale StandardScaler() 0 -> 0.4908963783251757
 2032/3780 0.250 12.196 scale MinMaxScaler()   0 -> 0.48532264282751253
 2033/3780 0.250 12.196 auto StandardScaler() 0 -> 0.49089637832518
 2034/3780 0.250 12.196 auto MinMaxScaler()   0 -> 0.5031931048549644
 2035/3780 0.250 12.196 0.01 StandardScaler() 0 -> 0.490676831718887
 2036/3780 0.250 12.196 0.01 MinMaxScaler()   0 -> 0.5493033688544985
 2037/3780 0.250 12.196 0.03162277660168379 StandardScaler() 0 -> 0.47926722928700766
 2038/3780 0.250 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5297145860218663
 2039/3780 0.250 12.196 0.1 StandardScaler() 0 -> 0.4977625886861399
 2040/3780 0.250 12.196 0.1 MinMaxScaler()   0 -> 0.5014851941176849
 2041/3780 0.250 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5027498199160906
 2042/3780 0.250 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4892181413328435
 2043/3780 0.250 12.196 1.0 StandardScaler() 0 -> 0.4963366059446143
 2044/3780 0.250 12.196 1.0 MinMaxScaler()   0 -> 0.4776543402134332
 2045/3780 0.250 15.476 scale StandardScaler() 0 -> 0.5010744211295538
 2046/3780 0.250 15.476 scale MinMaxScaler()   0 -> 0.48980583490509794
 2047/3780 0.250 15.476 auto StandardScaler() 0 -> 0.5010744211295571
 2048/3780 0.250 15.476 auto MinMaxScaler()   0 -> 0.5013594398989412
 2049/3780 0.250 15.476 0.01 StandardScaler() 0 -> 0.48960088302342103
 2050/3780 0.250 15.476 0.01 MinMaxScaler()   0 -> 0.5485482609357152
 2051/3780 0.250 15.476 0.03162277660168379 StandardScaler() 0 -> 0.479883936195964
 2052/3780 0.250 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5264896074692101
 2053/3780 0.250 15.476 0.1 StandardScaler() 0 -> 0.5129663612770471
 2054/3780 0.250 15.476 0.1 MinMaxScaler()   0 -> 0.5000792816524379
 2055/3780 0.250 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5054369829533941
 2056/3780 0.250 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.48819961270296597
 2057/3780 0.250 15.476 1.0 StandardScaler() 0 -> 0.4963366059446143
 2058/3780 0.250 15.476 1.0 MinMaxScaler()   0 -> 0.4785934093977966
 2059/3780 0.250 19.638 scale StandardScaler() 0 -> 0.5178356983806411
 2060/3780 0.250 19.638 scale MinMaxScaler()   0 -> 0.49718660220102145
 2061/3780 0.250 19.638 auto StandardScaler() 0 -> 0.5178356983806428
 2062/3780 0.250 19.638 auto MinMaxScaler()   0 -> 0.4999247282295187
 2063/3780 0.250 19.638 0.01 StandardScaler() 0 -> 0.48892012345939784
 2064/3780 0.250 19.638 0.01 MinMaxScaler()   0 -> 0.5473140623770402
 2065/3780 0.250 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4807278311837921
 2066/3780 0.250 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.523244115603101
 2067/3780 0.250 19.638 0.1 StandardScaler() 0 -> 0.530795191823495
 2068/3780 0.250 19.638 0.1 MinMaxScaler()   0 -> 0.49876813980410456
 2069/3780 0.250 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5082087921211152
 2070/3780 0.250 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.48737085640275213
 2071/3780 0.250 19.638 1.0 StandardScaler() 0 -> 0.4963366059446143
 2072/3780 0.250 19.638 1.0 MinMaxScaler()   0 -> 0.4790391847164506
 2073/3780 0.250 24.920 scale StandardScaler() 0 -> 0.5363098291070574
 2074/3780 0.250 24.920 scale MinMaxScaler()   0 -> 0.508148844511907
 2075/3780 0.250 24.920 auto StandardScaler() 0 -> 0.5363098291070604
 2076/3780 0.250 24.920 auto MinMaxScaler()   0 -> 0.4987078991895591
 2077/3780 0.250 24.920 0.01 StandardScaler() 0 -> 0.4877044943121945
 2078/3780 0.250 24.920 0.01 MinMaxScaler()   0 -> 0.5461178357066849
 2079/3780 0.250 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4820964779136471
 2080/3780 0.250 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5202797841807136
 2081/3780 0.250 24.920 0.1 StandardScaler() 0 -> 0.5497236683308434
 2082/3780 0.250 24.920 0.1 MinMaxScaler()   0 -> 0.4981322907399403
 2083/3780 0.250 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5101755207986259
 2084/3780 0.250 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.4863561064943765
 2085/3780 0.250 24.920 1.0 StandardScaler() 0 -> 0.4963366059446143
 2086/3780 0.250 24.920 1.0 MinMaxScaler()   0 -> 0.48066689378684196
 2087/3780 0.250 31.623 scale StandardScaler() 0 -> 0.557008434328964
 2088/3780 0.250 31.623 scale MinMaxScaler()   0 -> 0.5208442573839079
 2089/3780 0.250 31.623 auto StandardScaler() 0 -> 0.5570084343289697
 2090/3780 0.250 31.623 auto MinMaxScaler()   0 -> 0.49821595089713294
 2091/3780 0.250 31.623 0.01 StandardScaler() 0 -> 0.4878412391997227
 2092/3780 0.250 31.623 0.01 MinMaxScaler()   0 -> 0.5443536721473826
 2093/3780 0.250 31.623 0.03162277660168379 StandardScaler() 0 -> 0.48468151340001114
 2094/3780 0.250 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5169642768418242
 2095/3780 0.250 31.623 0.1 StandardScaler() 0 -> 0.5695772633169881
 2096/3780 0.250 31.623 0.1 MinMaxScaler()   0 -> 0.49729698999449634
 2097/3780 0.250 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5112599468693579
 2098/3780 0.250 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4855788743076323
 2099/3780 0.250 31.623 1.0 StandardScaler() 0 -> 0.4963366059446143
 2100/3780 0.250 31.623 1.0 MinMaxScaler()   0 -> 0.484844119318363
 2101/3780 0.300 0.032 scale StandardScaler() 0 -> 0.5309106855175726
 2102/3780 0.300 0.032 scale MinMaxScaler()   0 -> 0.5267412555923943
 2103/3780 0.300 0.032 auto StandardScaler() 0 -> 0.5309106855175726
 2104/3780 0.300 0.032 auto MinMaxScaler()   0 -> 0.630900169897446
 2105/3780 0.300 0.032 0.01 StandardScaler() 0 -> 0.5801489439779756
 2106/3780 0.300 0.032 0.01 MinMaxScaler()   0 -> 0.7729141550465775
 2107/3780 0.300 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5413414477677444
 2108/3780 0.300 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7200497951104273
 2109/3780 0.300 0.032 0.1 StandardScaler() 0 -> 0.5322146869059509
 2110/3780 0.300 0.032 0.1 MinMaxScaler()   0 -> 0.6240757534459144
 2111/3780 0.300 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6065071347190399
 2112/3780 0.300 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5691131551482673
 2113/3780 0.300 0.032 1.0 StandardScaler() 0 -> 0.7704640649091209
 2114/3780 0.300 0.032 1.0 MinMaxScaler()   0 -> 0.5341249621327414
 2115/3780 0.300 0.040 scale StandardScaler() 0 -> 0.5240849309563801
 2116/3780 0.300 0.040 scale MinMaxScaler()   0 -> 0.5207872839256115
 2117/3780 0.300 0.040 auto StandardScaler() 0 -> 0.5240849309563801
 2118/3780 0.300 0.040 auto MinMaxScaler()   0 -> 0.6145833749250671
 2119/3780 0.300 0.040 0.01 StandardScaler() 0 -> 0.5700939330117736
 2120/3780 0.300 0.040 0.01 MinMaxScaler()   0 -> 0.765642316895543
 2121/3780 0.300 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5333223137309296
 2122/3780 0.300 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7018222269140314
 2123/3780 0.300 0.040 0.1 StandardScaler() 0 -> 0.5254978271340672
 2124/3780 0.300 0.040 0.1 MinMaxScaler()   0 -> 0.6093323477199775
 2125/3780 0.300 0.040 0.31622776601683794 StandardScaler() 0 -> 0.5885118390389538
 2126/3780 0.300 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.561158839742289
 2127/3780 0.300 0.040 1.0 StandardScaler() 0 -> 0.7626581365824762
 2128/3780 0.300 0.040 1.0 MinMaxScaler()   0 -> 0.5280635940895596
 2129/3780 0.300 0.051 scale StandardScaler() 0 -> 0.5175958129231534
 2130/3780 0.300 0.051 scale MinMaxScaler()   0 -> 0.5147308994403935
 2131/3780 0.300 0.051 auto StandardScaler() 0 -> 0.5175958129231534
 2132/3780 0.300 0.051 auto MinMaxScaler()   0 -> 0.6013465109996287
 2133/3780 0.300 0.051 0.01 StandardScaler() 0 -> 0.5617983304744752
 2134/3780 0.300 0.051 0.01 MinMaxScaler()   0 -> 0.7565956623421353
 2135/3780 0.300 0.051 0.03162277660168379 StandardScaler() 0 -> 0.527035686128294
 2136/3780 0.300 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6809823214298986
 2137/3780 0.300 0.051 0.1 StandardScaler() 0 -> 0.5185742577096398
 2138/3780 0.300 0.051 0.1 MinMaxScaler()   0 -> 0.5971201094834969
 2139/3780 0.300 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5733744699213833
 2140/3780 0.300 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5536655919197845
 2141/3780 0.300 0.051 1.0 StandardScaler() 0 -> 0.7530636854814468
 2142/3780 0.300 0.051 1.0 MinMaxScaler()   0 -> 0.5230819055093936
 2143/3780 0.300 0.065 scale StandardScaler() 0 -> 0.5111996104690165
 2144/3780 0.300 0.065 scale MinMaxScaler()   0 -> 0.5098320919030336
 2145/3780 0.300 0.065 auto StandardScaler() 0 -> 0.5111996104690166
 2146/3780 0.300 0.065 auto MinMaxScaler()   0 -> 0.5900916673663662
 2147/3780 0.300 0.065 0.01 StandardScaler() 0 -> 0.5546807922682042
 2148/3780 0.300 0.065 0.01 MinMaxScaler()   0 -> 0.7455431806737551
 2149/3780 0.300 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5217245929106494
 2150/3780 0.300 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6583412284373259
 2151/3780 0.300 0.065 0.1 StandardScaler() 0 -> 0.5116700684097442
 2152/3780 0.300 0.065 0.1 MinMaxScaler()   0 -> 0.5855585507641753
 2153/3780 0.300 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5593907422401295
 2154/3780 0.300 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5479015855531811
 2155/3780 0.300 0.065 1.0 StandardScaler() 0 -> 0.7414698840168747
 2156/3780 0.300 0.065 1.0 MinMaxScaler()   0 -> 0.5177243703738531
 2157/3780 0.300 0.082 scale StandardScaler() 0 -> 0.5047779685235412
 2158/3780 0.300 0.082 scale MinMaxScaler()   0 -> 0.5053236005579782
 2159/3780 0.300 0.082 auto StandardScaler() 0 -> 0.5047779685235412
 2160/3780 0.300 0.082 auto MinMaxScaler()   0 -> 0.5798061421115609
 2161/3780 0.300 0.082 0.01 StandardScaler() 0 -> 0.5484264712267511
 2162/3780 0.300 0.082 0.01 MinMaxScaler()   0 -> 0.7321215470499642
 2163/3780 0.300 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5158497054320569
 2164/3780 0.300 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6361857022809506
 2165/3780 0.300 0.082 0.1 StandardScaler() 0 -> 0.504884583635483
 2166/3780 0.300 0.082 0.1 MinMaxScaler()   0 -> 0.5766416787174798
 2167/3780 0.300 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5459379169708932
 2168/3780 0.300 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5427793247356814
 2169/3780 0.300 0.082 1.0 StandardScaler() 0 -> 0.7276050254413131
 2170/3780 0.300 0.082 1.0 MinMaxScaler()   0 -> 0.5130397955357425
 2171/3780 0.300 0.104 scale StandardScaler() 0 -> 0.49935530357274965
 2172/3780 0.300 0.104 scale MinMaxScaler()   0 -> 0.5004219415751142
 2173/3780 0.300 0.104 auto StandardScaler() 0 -> 0.49935530357274965
 2174/3780 0.300 0.104 auto MinMaxScaler()   0 -> 0.5723597364892418
 2175/3780 0.300 0.104 0.01 StandardScaler() 0 -> 0.5428255275228251
 2176/3780 0.300 0.104 0.01 MinMaxScaler()   0 -> 0.7160671180337257
 2177/3780 0.300 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5103423479689041
 2178/3780 0.300 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6190686889802247
 2179/3780 0.300 0.104 0.1 StandardScaler() 0 -> 0.4992647058860546
 2180/3780 0.300 0.104 0.1 MinMaxScaler()   0 -> 0.5697060979467093
 2181/3780 0.300 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5327675404750781
 2182/3780 0.300 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5374874215386197
 2183/3780 0.300 0.104 1.0 StandardScaler() 0 -> 0.7110725306375385
 2184/3780 0.300 0.104 1.0 MinMaxScaler()   0 -> 0.5087124377845037
 2185/3780 0.300 0.132 scale StandardScaler() 0 -> 0.49402966883516664
 2186/3780 0.300 0.132 scale MinMaxScaler()   0 -> 0.4960131037554815
 2187/3780 0.300 0.132 auto StandardScaler() 0 -> 0.49402966883516664
 2188/3780 0.300 0.132 auto MinMaxScaler()   0 -> 0.5651371056378612
 2189/3780 0.300 0.132 0.01 StandardScaler() 0 -> 0.5373902439367733
 2190/3780 0.300 0.132 0.01 MinMaxScaler()   0 -> 0.6972465662196473
 2191/3780 0.300 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5061453999206132
 2192/3780 0.300 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6052689800660099
 2193/3780 0.300 0.132 0.1 StandardScaler() 0 -> 0.49422948858574745
 2194/3780 0.300 0.132 0.1 MinMaxScaler()   0 -> 0.5623677576935345
 2195/3780 0.300 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5213204020132592
 2196/3780 0.300 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5324161236690806
 2197/3780 0.300 0.132 1.0 StandardScaler() 0 -> 0.6915471970870062
 2198/3780 0.300 0.132 1.0 MinMaxScaler()   0 -> 0.5051671505423884
 2199/3780 0.300 0.168 scale StandardScaler() 0 -> 0.49002579470581736
 2200/3780 0.300 0.168 scale MinMaxScaler()   0 -> 0.49189410882751883
 2201/3780 0.300 0.168 auto StandardScaler() 0 -> 0.4900257947058175
 2202/3780 0.300 0.168 auto MinMaxScaler()   0 -> 0.5598126433590317
 2203/3780 0.300 0.168 0.01 StandardScaler() 0 -> 0.531893912882314
 2204/3780 0.300 0.168 0.01 MinMaxScaler()   0 -> 0.6758594675544279
 2205/3780 0.300 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5020551337258392
 2206/3780 0.300 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.5945734021078984
 2207/3780 0.300 0.168 0.1 StandardScaler() 0 -> 0.4900887070681936
 2208/3780 0.300 0.168 0.1 MinMaxScaler()   0 -> 0.5573741615039612
 2209/3780 0.300 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5115956392312793
 2210/3780 0.300 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5285261714244788
 2211/3780 0.300 0.168 1.0 StandardScaler() 0 -> 0.669215459867898
 2212/3780 0.300 0.168 1.0 MinMaxScaler()   0 -> 0.5015545025023496
 2213/3780 0.300 0.213 scale StandardScaler() 0 -> 0.48673848588921037
 2214/3780 0.300 0.213 scale MinMaxScaler()   0 -> 0.4883620105029099
 2215/3780 0.300 0.213 auto StandardScaler() 0 -> 0.48673848588921037
 2216/3780 0.300 0.213 auto MinMaxScaler()   0 -> 0.5555596014808244
 2217/3780 0.300 0.213 0.01 StandardScaler() 0 -> 0.527263118990696
 2218/3780 0.300 0.213 0.01 MinMaxScaler()   0 -> 0.6530060338895584
 2219/3780 0.300 0.213 0.03162277660168379 StandardScaler() 0 -> 0.49966969812293116
 2220/3780 0.300 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5841718020782439
 2221/3780 0.300 0.213 0.1 StandardScaler() 0 -> 0.48670291617390243
 2222/3780 0.300 0.213 0.1 MinMaxScaler()   0 -> 0.553626064115789
 2223/3780 0.300 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5022411906835256
 2224/3780 0.300 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5246119557954972
 2225/3780 0.300 0.213 1.0 StandardScaler() 0 -> 0.6438612314471294
 2226/3780 0.300 0.213 1.0 MinMaxScaler()   0 -> 0.49848546854794645
 2227/3780 0.300 0.270 scale StandardScaler() 0 -> 0.4839094824406396
 2228/3780 0.300 0.270 scale MinMaxScaler()   0 -> 0.48569312224225936
 2229/3780 0.300 0.270 auto StandardScaler() 0 -> 0.4839094824406395
 2230/3780 0.300 0.270 auto MinMaxScaler()   0 -> 0.5518441621634739
 2231/3780 0.300 0.270 0.01 StandardScaler() 0 -> 0.5236026240429256
 2232/3780 0.300 0.270 0.01 MinMaxScaler()   0 -> 0.6320304007621316
 2233/3780 0.300 0.270 0.03162277660168379 StandardScaler() 0 -> 0.4964572529251156
 2234/3780 0.300 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5761416545815017
 2235/3780 0.300 0.270 0.1 StandardScaler() 0 -> 0.4837260625372825
 2236/3780 0.300 0.270 0.1 MinMaxScaler()   0 -> 0.5496726923889792
 2237/3780 0.300 0.270 0.31622776601683794 StandardScaler() 0 -> 0.49377927777942593
 2238/3780 0.300 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5210125451959436
 2239/3780 0.300 0.270 1.0 StandardScaler() 0 -> 0.6166339289061996
 2240/3780 0.300 0.270 1.0 MinMaxScaler()   0 -> 0.49597701743601075
 2241/3780 0.300 0.342 scale StandardScaler() 0 -> 0.48145244449629626
 2242/3780 0.300 0.342 scale MinMaxScaler()   0 -> 0.4832335298628157
 2243/3780 0.300 0.342 auto StandardScaler() 0 -> 0.48145244449629626
 2244/3780 0.300 0.342 auto MinMaxScaler()   0 -> 0.547997840002682
 2245/3780 0.300 0.342 0.01 StandardScaler() 0 -> 0.5199082534823655
 2246/3780 0.300 0.342 0.01 MinMaxScaler()   0 -> 0.6161206985935993
 2247/3780 0.300 0.342 0.03162277660168379 StandardScaler() 0 -> 0.4941360240326788
 2248/3780 0.300 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5695422886714135
 2249/3780 0.300 0.342 0.1 StandardScaler() 0 -> 0.4811462093717247
 2250/3780 0.300 0.342 0.1 MinMaxScaler()   0 -> 0.5463989656019929
 2251/3780 0.300 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4858561804614303
 2252/3780 0.300 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5175380445330909
 2253/3780 0.300 0.342 1.0 StandardScaler() 0 -> 0.5896848782592886
 2254/3780 0.300 0.342 1.0 MinMaxScaler()   0 -> 0.493077201977475
 2255/3780 0.300 0.434 scale StandardScaler() 0 -> 0.47922108990175344
 2256/3780 0.300 0.434 scale MinMaxScaler()   0 -> 0.4812377599186876
 2257/3780 0.300 0.434 auto StandardScaler() 0 -> 0.4792210899017533
 2258/3780 0.300 0.434 auto MinMaxScaler()   0 -> 0.5450889304321817
 2259/3780 0.300 0.434 0.01 StandardScaler() 0 -> 0.5163690091641319
 2260/3780 0.300 0.434 0.01 MinMaxScaler()   0 -> 0.6031484267369979
 2261/3780 0.300 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49099547406294813
 2262/3780 0.300 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.563693591521745
 2263/3780 0.300 0.434 0.1 StandardScaler() 0 -> 0.4784974456110251
 2264/3780 0.300 0.434 0.1 MinMaxScaler()   0 -> 0.5431449707132789
 2265/3780 0.300 0.434 0.31622776601683794 StandardScaler() 0 -> 0.478975818304382
 2266/3780 0.300 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.514311333293955
 2267/3780 0.300 0.434 1.0 StandardScaler() 0 -> 0.5635332773849574
 2268/3780 0.300 0.434 1.0 MinMaxScaler()   0 -> 0.490098476311761
 2269/3780 0.300 0.551 scale StandardScaler() 0 -> 0.47688770725064106
 2270/3780 0.300 0.551 scale MinMaxScaler()   0 -> 0.4789998879651696
 2271/3780 0.300 0.551 auto StandardScaler() 0 -> 0.47688770725064106
 2272/3780 0.300 0.551 auto MinMaxScaler()   0 -> 0.5422784837112979
 2273/3780 0.300 0.551 0.01 StandardScaler() 0 -> 0.5128625922316726
 2274/3780 0.300 0.551 0.01 MinMaxScaler()   0 -> 0.5924568450849627
 2275/3780 0.300 0.551 0.03162277660168379 StandardScaler() 0 -> 0.48845819033017657
 2276/3780 0.300 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.559675655215073
 2277/3780 0.300 0.551 0.1 StandardScaler() 0 -> 0.47618967061863593
 2278/3780 0.300 0.551 0.1 MinMaxScaler()   0 -> 0.5402276106321321
 2279/3780 0.300 0.551 0.31622776601683794 StandardScaler() 0 -> 0.4737163590656737
 2280/3780 0.300 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.510559518962606
 2281/3780 0.300 0.551 1.0 StandardScaler() 0 -> 0.5397085206896689
 2282/3780 0.300 0.551 1.0 MinMaxScaler()   0 -> 0.4883774614559204
 2283/3780 0.300 0.700 scale StandardScaler() 0 -> 0.4749511416545367
 2284/3780 0.300 0.700 scale MinMaxScaler()   0 -> 0.4762760658091971
 2285/3780 0.300 0.700 auto StandardScaler() 0 -> 0.4749511416545367
 2286/3780 0.300 0.700 auto MinMaxScaler()   0 -> 0.5394794228652482
 2287/3780 0.300 0.700 0.01 StandardScaler() 0 -> 0.5090264259490345
 2288/3780 0.300 0.700 0.01 MinMaxScaler()   0 -> 0.5829514286821006
 2289/3780 0.300 0.700 0.03162277660168379 StandardScaler() 0 -> 0.48646804806255334
 2290/3780 0.300 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5567518154720337
 2291/3780 0.300 0.700 0.1 StandardScaler() 0 -> 0.47379665407896937
 2292/3780 0.300 0.700 0.1 MinMaxScaler()   0 -> 0.537438356046622
 2293/3780 0.300 0.700 0.31622776601683794 StandardScaler() 0 -> 0.4692050158166478
 2294/3780 0.300 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5071949901314977
 2295/3780 0.300 0.700 1.0 StandardScaler() 0 -> 0.5223024002121848
 2296/3780 0.300 0.700 1.0 MinMaxScaler()   0 -> 0.4869461776998132
 2297/3780 0.300 0.888 scale StandardScaler() 0 -> 0.473336216241836
 2298/3780 0.300 0.888 scale MinMaxScaler()   0 -> 0.4739533788122321
 2299/3780 0.300 0.888 auto StandardScaler() 0 -> 0.47333621624183575
 2300/3780 0.300 0.888 auto MinMaxScaler()   0 -> 0.5368753453005045
 2301/3780 0.300 0.888 0.01 StandardScaler() 0 -> 0.5060747634182764
 2302/3780 0.300 0.888 0.01 MinMaxScaler()   0 -> 0.5751547200123482
 2303/3780 0.300 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4852820290839905
 2304/3780 0.300 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5538233802860622
 2305/3780 0.300 0.888 0.1 StandardScaler() 0 -> 0.4724736880414196
 2306/3780 0.300 0.888 0.1 MinMaxScaler()   0 -> 0.5343483038598191
 2307/3780 0.300 0.888 0.31622776601683794 StandardScaler() 0 -> 0.4663103033049838
 2308/3780 0.300 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.505003383364187
 2309/3780 0.300 0.888 1.0 StandardScaler() 0 -> 0.5131443073722973
 2310/3780 0.300 0.888 1.0 MinMaxScaler()   0 -> 0.4849697853307124
 2311/3780 0.300 1.126 scale StandardScaler() 0 -> 0.47214451155127307
 2312/3780 0.300 1.126 scale MinMaxScaler()   0 -> 0.4723845777232165
 2313/3780 0.300 1.126 auto StandardScaler() 0 -> 0.47214451155127274
 2314/3780 0.300 1.126 auto MinMaxScaler()   0 -> 0.5336174449516438
 2315/3780 0.300 1.126 0.01 StandardScaler() 0 -> 0.5030473835930435
 2316/3780 0.300 1.126 0.01 MinMaxScaler()   0 -> 0.5687591603192298
 2317/3780 0.300 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48413617474687837
 2318/3780 0.300 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5514697761847045
 2319/3780 0.300 1.126 0.1 StandardScaler() 0 -> 0.47116967701731566
 2320/3780 0.300 1.126 0.1 MinMaxScaler()   0 -> 0.5314983682170011
 2321/3780 0.300 1.126 0.31622776601683794 StandardScaler() 0 -> 0.4649622264525844
 2322/3780 0.300 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5023559267456631
 2323/3780 0.300 1.126 1.0 StandardScaler() 0 -> 0.508001760835214
 2324/3780 0.300 1.126 1.0 MinMaxScaler()   0 -> 0.48335373065151305
 2325/3780 0.300 1.429 scale StandardScaler() 0 -> 0.471454393924631
 2326/3780 0.300 1.429 scale MinMaxScaler()   0 -> 0.47140454593399556
 2327/3780 0.300 1.429 auto StandardScaler() 0 -> 0.471454393924631
 2328/3780 0.300 1.429 auto MinMaxScaler()   0 -> 0.5306860800463574
 2329/3780 0.300 1.429 0.01 StandardScaler() 0 -> 0.5004227011786857
 2330/3780 0.300 1.429 0.01 MinMaxScaler()   0 -> 0.5639223217337103
 2331/3780 0.300 1.429 0.03162277660168379 StandardScaler() 0 -> 0.4835063307549308
 2332/3780 0.300 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5496002484663265
 2333/3780 0.300 1.429 0.1 StandardScaler() 0 -> 0.47080170900893625
 2334/3780 0.300 1.429 0.1 MinMaxScaler()   0 -> 0.5276244917992
 2335/3780 0.300 1.429 0.31622776601683794 StandardScaler() 0 -> 0.46392260103201094
 2336/3780 0.300 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.4991952465180323
 2337/3780 0.300 1.429 1.0 StandardScaler() 0 -> 0.5039546840538818
 2338/3780 0.300 1.429 1.0 MinMaxScaler()   0 -> 0.4818330375760418
 2339/3780 0.300 1.814 scale StandardScaler() 0 -> 0.47127514663388176
 2340/3780 0.300 1.814 scale MinMaxScaler()   0 -> 0.47139121384804844
 2341/3780 0.300 1.814 auto StandardScaler() 0 -> 0.47127514663388165
 2342/3780 0.300 1.814 auto MinMaxScaler()   0 -> 0.5267988060101356
 2343/3780 0.300 1.814 0.01 StandardScaler() 0 -> 0.49797496890184606
 2344/3780 0.300 1.814 0.01 MinMaxScaler()   0 -> 0.560232892368041
 2345/3780 0.300 1.814 0.03162277660168379 StandardScaler() 0 -> 0.4822066717025493
 2346/3780 0.300 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5479342701304026
 2347/3780 0.300 1.814 0.1 StandardScaler() 0 -> 0.4699894897812287
 2348/3780 0.300 1.814 0.1 MinMaxScaler()   0 -> 0.5244158129250446
 2349/3780 0.300 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4655679265655848
 2350/3780 0.300 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.49718439560999833
 2351/3780 0.300 1.814 1.0 StandardScaler() 0 -> 0.5022140594836868
 2352/3780 0.300 1.814 1.0 MinMaxScaler()   0 -> 0.48023944115936273
 2353/3780 0.300 2.302 scale StandardScaler() 0 -> 0.4704929621097353
 2354/3780 0.300 2.302 scale MinMaxScaler()   0 -> 0.47186541372118107
 2355/3780 0.300 2.302 auto StandardScaler() 0 -> 0.4704929621097347
 2356/3780 0.300 2.302 auto MinMaxScaler()   0 -> 0.5240431674762557
 2357/3780 0.300 2.302 0.01 StandardScaler() 0 -> 0.4957831590524921
 2358/3780 0.300 2.302 0.01 MinMaxScaler()   0 -> 0.5573178952662287
 2359/3780 0.300 2.302 0.03162277660168379 StandardScaler() 0 -> 0.48110593137307656
 2360/3780 0.300 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5465800339887831
 2361/3780 0.300 2.302 0.1 StandardScaler() 0 -> 0.4693313911739583
 2362/3780 0.300 2.302 0.1 MinMaxScaler()   0 -> 0.5218564468329644
 2363/3780 0.300 2.302 0.31622776601683794 StandardScaler() 0 -> 0.46863264622756223
 2364/3780 0.300 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.49505336618325585
 2365/3780 0.300 2.302 1.0 StandardScaler() 0 -> 0.5018408945213745
 2366/3780 0.300 2.302 1.0 MinMaxScaler()   0 -> 0.47901168156164337
 2367/3780 0.300 2.921 scale StandardScaler() 0 -> 0.4704177891864765
 2368/3780 0.300 2.921 scale MinMaxScaler()   0 -> 0.47199350376332777
 2369/3780 0.300 2.921 auto StandardScaler() 0 -> 0.4704177891864772
 2370/3780 0.300 2.921 auto MinMaxScaler()   0 -> 0.5213379041973992
 2371/3780 0.300 2.921 0.01 StandardScaler() 0 -> 0.4945169211037346
 2372/3780 0.300 2.921 0.01 MinMaxScaler()   0 -> 0.5553917778981293
 2373/3780 0.300 2.921 0.03162277660168379 StandardScaler() 0 -> 0.4803033173785804
 2374/3780 0.300 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5450623795278097
 2375/3780 0.300 2.921 0.1 StandardScaler() 0 -> 0.4693552301390658
 2376/3780 0.300 2.921 0.1 MinMaxScaler()   0 -> 0.5185571532337635
 2377/3780 0.300 2.921 0.31622776601683794 StandardScaler() 0 -> 0.47194053142327314
 2378/3780 0.300 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49325723330503674
 2379/3780 0.300 2.921 1.0 StandardScaler() 0 -> 0.501717361941421
 2380/3780 0.300 2.921 1.0 MinMaxScaler()   0 -> 0.47803387117862295
 2381/3780 0.300 3.707 scale StandardScaler() 0 -> 0.470879892356816
 2382/3780 0.300 3.707 scale MinMaxScaler()   0 -> 0.4728518813138189
 2383/3780 0.300 3.707 auto StandardScaler() 0 -> 0.47087989235681577
 2384/3780 0.300 3.707 auto MinMaxScaler()   0 -> 0.5181247142907836
 2385/3780 0.300 3.707 0.01 StandardScaler() 0 -> 0.4932834482573652
 2386/3780 0.300 3.707 0.01 MinMaxScaler()   0 -> 0.5535178233556818
 2387/3780 0.300 3.707 0.03162277660168379 StandardScaler() 0 -> 0.47950995577079397
 2388/3780 0.300 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5430623089082679
 2389/3780 0.300 3.707 0.1 StandardScaler() 0 -> 0.4702903208868745
 2390/3780 0.300 3.707 0.1 MinMaxScaler()   0 -> 0.5154828154519907
 2391/3780 0.300 3.707 0.31622776601683794 StandardScaler() 0 -> 0.4768835126148398
 2392/3780 0.300 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4918336487102864
 2393/3780 0.300 3.707 1.0 StandardScaler() 0 -> 0.5017289250082165
 2394/3780 0.300 3.707 1.0 MinMaxScaler()   0 -> 0.47722605050681005
 2395/3780 0.300 4.703 scale StandardScaler() 0 -> 0.47233811527405406
 2396/3780 0.300 4.703 scale MinMaxScaler()   0 -> 0.4738182747441257
 2397/3780 0.300 4.703 auto StandardScaler() 0 -> 0.4723381152740536
 2398/3780 0.300 4.703 auto MinMaxScaler()   0 -> 0.5148490038993186
 2399/3780 0.300 4.703 0.01 StandardScaler() 0 -> 0.4921850983531237
 2400/3780 0.300 4.703 0.01 MinMaxScaler()   0 -> 0.5524997271662678
 2401/3780 0.300 4.703 0.03162277660168379 StandardScaler() 0 -> 0.4781913568023007
 2402/3780 0.300 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5408728708817981
 2403/3780 0.300 4.703 0.1 StandardScaler() 0 -> 0.4729750494359337
 2404/3780 0.300 4.703 0.1 MinMaxScaler()   0 -> 0.5118197132967813
 2405/3780 0.300 4.703 0.31622776601683794 StandardScaler() 0 -> 0.48170807613505917
 2406/3780 0.300 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.49125137053566365
 2407/3780 0.300 4.703 1.0 StandardScaler() 0 -> 0.5017352440249349
 2408/3780 0.300 4.703 1.0 MinMaxScaler()   0 -> 0.476253726154567
 2409/3780 0.300 5.968 scale StandardScaler() 0 -> 0.47519399614870594
 2410/3780 0.300 5.968 scale MinMaxScaler()   0 -> 0.47425786628687266
 2411/3780 0.300 5.968 auto StandardScaler() 0 -> 0.4751939961487055
 2412/3780 0.300 5.968 auto MinMaxScaler()   0 -> 0.511405346938106
 2413/3780 0.300 5.968 0.01 StandardScaler() 0 -> 0.49165332797288136
 2414/3780 0.300 5.968 0.01 MinMaxScaler()   0 -> 0.5518663194638204
 2415/3780 0.300 5.968 0.03162277660168379 StandardScaler() 0 -> 0.4776341085798072
 2416/3780 0.300 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.538507535113924
 2417/3780 0.300 5.968 0.1 StandardScaler() 0 -> 0.4761056853483467
 2418/3780 0.300 5.968 0.1 MinMaxScaler()   0 -> 0.5092826054501132
 2419/3780 0.300 5.968 0.31622776601683794 StandardScaler() 0 -> 0.4874342954173207
 2420/3780 0.300 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.49070485036526995
 2421/3780 0.300 5.968 1.0 StandardScaler() 0 -> 0.501740160463844
 2422/3780 0.300 5.968 1.0 MinMaxScaler()   0 -> 0.47562431201511285
 2423/3780 0.300 7.574 scale StandardScaler() 0 -> 0.4788457854305436
 2424/3780 0.300 7.574 scale MinMaxScaler()   0 -> 0.4763063043068137
 2425/3780 0.300 7.574 auto StandardScaler() 0 -> 0.478845785430543
 2426/3780 0.300 7.574 auto MinMaxScaler()   0 -> 0.5087985958272004
 2427/3780 0.300 7.574 0.01 StandardScaler() 0 -> 0.49081984973611475
 2428/3780 0.300 7.574 0.01 MinMaxScaler()   0 -> 0.5513201618592399
 2429/3780 0.300 7.574 0.03162277660168379 StandardScaler() 0 -> 0.47768442800622674
 2430/3780 0.300 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5359564286100454
 2431/3780 0.300 7.574 0.1 StandardScaler() 0 -> 0.48084001905944335
 2432/3780 0.300 7.574 0.1 MinMaxScaler()   0 -> 0.507066765119704
 2433/3780 0.300 7.574 0.31622776601683794 StandardScaler() 0 -> 0.49183090930624934
 2434/3780 0.300 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.48992929551391323
 2435/3780 0.300 7.574 1.0 StandardScaler() 0 -> 0.5017399932216955
 2436/3780 0.300 7.574 1.0 MinMaxScaler()   0 -> 0.47605346148353583
 2437/3780 0.300 9.611 scale StandardScaler() 0 -> 0.48352088066273025
 2438/3780 0.300 9.611 scale MinMaxScaler()   0 -> 0.47975586482531024
 2439/3780 0.300 9.611 auto StandardScaler() 0 -> 0.48352088066272797
 2440/3780 0.300 9.611 auto MinMaxScaler()   0 -> 0.506483059957065
 2441/3780 0.300 9.611 0.01 StandardScaler() 0 -> 0.49056332607517933
 2442/3780 0.300 9.611 0.01 MinMaxScaler()   0 -> 0.5505566447725544
 2443/3780 0.300 9.611 0.03162277660168379 StandardScaler() 0 -> 0.477853020709711
 2444/3780 0.300 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5329282857882326
 2445/3780 0.300 9.611 0.1 StandardScaler() 0 -> 0.4890155673752943
 2446/3780 0.300 9.611 0.1 MinMaxScaler()   0 -> 0.5044144957160311
 2447/3780 0.300 9.611 0.31622776601683794 StandardScaler() 0 -> 0.49606590138347967
 2448/3780 0.300 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4893270280839452
 2449/3780 0.300 9.611 1.0 StandardScaler() 0 -> 0.5017410981188717
 2450/3780 0.300 9.611 1.0 MinMaxScaler()   0 -> 0.4763880416104886
 2451/3780 0.300 12.196 scale StandardScaler() 0 -> 0.4912745035159866
 2452/3780 0.300 12.196 scale MinMaxScaler()   0 -> 0.48312349843804
 2453/3780 0.300 12.196 auto StandardScaler() 0 -> 0.49127450351598584
 2454/3780 0.300 12.196 auto MinMaxScaler()   0 -> 0.5038636910888616
 2455/3780 0.300 12.196 0.01 StandardScaler() 0 -> 0.4898993791713852
 2456/3780 0.300 12.196 0.01 MinMaxScaler()   0 -> 0.5494463480363828
 2457/3780 0.300 12.196 0.03162277660168379 StandardScaler() 0 -> 0.47837065277569346
 2458/3780 0.300 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5298096259841824
 2459/3780 0.300 12.196 0.1 StandardScaler() 0 -> 0.4989835967572891
 2460/3780 0.300 12.196 0.1 MinMaxScaler()   0 -> 0.5017429826187844
 2461/3780 0.300 12.196 0.31622776601683794 StandardScaler() 0 -> 0.49914421131870795
 2462/3780 0.300 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4888869403802749
 2463/3780 0.300 12.196 1.0 StandardScaler() 0 -> 0.5017410981188717
 2464/3780 0.300 12.196 1.0 MinMaxScaler()   0 -> 0.47657936689130703
 2465/3780 0.300 15.476 scale StandardScaler() 0 -> 0.5027273604249735
 2466/3780 0.300 15.476 scale MinMaxScaler()   0 -> 0.48875945940860926
 2467/3780 0.300 15.476 auto StandardScaler() 0 -> 0.5027273604249727
 2468/3780 0.300 15.476 auto MinMaxScaler()   0 -> 0.5015177207324049
 2469/3780 0.300 15.476 0.01 StandardScaler() 0 -> 0.48941246945310213
 2470/3780 0.300 15.476 0.01 MinMaxScaler()   0 -> 0.5483056822710649
 2471/3780 0.300 15.476 0.03162277660168379 StandardScaler() 0 -> 0.47902003193571546
 2472/3780 0.300 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5266476491140714
 2473/3780 0.300 15.476 0.1 StandardScaler() 0 -> 0.510591054809164
 2474/3780 0.300 15.476 0.1 MinMaxScaler()   0 -> 0.5001476217651996
 2475/3780 0.300 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5017360487472492
 2476/3780 0.300 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.4882491899043026
 2477/3780 0.300 15.476 1.0 StandardScaler() 0 -> 0.5017410981188717
 2478/3780 0.300 15.476 1.0 MinMaxScaler()   0 -> 0.47693255476131585
 2479/3780 0.300 19.638 scale StandardScaler() 0 -> 0.5158683392356246
 2480/3780 0.300 19.638 scale MinMaxScaler()   0 -> 0.4973546425391559
 2481/3780 0.300 19.638 auto StandardScaler() 0 -> 0.515868339235619
 2482/3780 0.300 19.638 auto MinMaxScaler()   0 -> 0.5001872097443482
 2483/3780 0.300 19.638 0.01 StandardScaler() 0 -> 0.4890434117910723
 2484/3780 0.300 19.638 0.01 MinMaxScaler()   0 -> 0.547186195717212
 2485/3780 0.300 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4802393210236597
 2486/3780 0.300 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5239666420593648
 2487/3780 0.300 19.638 0.1 StandardScaler() 0 -> 0.5256013655476138
 2488/3780 0.300 19.638 0.1 MinMaxScaler()   0 -> 0.4983311159455379
 2489/3780 0.300 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5042536896175628
 2490/3780 0.300 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.48718698991413634
 2491/3780 0.300 19.638 1.0 StandardScaler() 0 -> 0.5017410981188717
 2492/3780 0.300 19.638 1.0 MinMaxScaler()   0 -> 0.4778112097438747
 2493/3780 0.300 24.920 scale StandardScaler() 0 -> 0.5313411810790262
 2494/3780 0.300 24.920 scale MinMaxScaler()   0 -> 0.5078845878898465
 2495/3780 0.300 24.920 auto StandardScaler() 0 -> 0.5313411810790284
 2496/3780 0.300 24.920 auto MinMaxScaler()   0 -> 0.49822362857275987
 2497/3780 0.300 24.920 0.01 StandardScaler() 0 -> 0.48816916200632504
 2498/3780 0.300 24.920 0.01 MinMaxScaler()   0 -> 0.5455366013831081
 2499/3780 0.300 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4821148543006461
 2500/3780 0.300 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5210449005200753
 2501/3780 0.300 24.920 0.1 StandardScaler() 0 -> 0.5423087492026393
 2502/3780 0.300 24.920 0.1 MinMaxScaler()   0 -> 0.49705907015586487
 2503/3780 0.300 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5051900735510965
 2504/3780 0.300 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.4862422816907026
 2505/3780 0.300 24.920 1.0 StandardScaler() 0 -> 0.5017410981188717
 2506/3780 0.300 24.920 1.0 MinMaxScaler()   0 -> 0.4802129528121122
 2507/3780 0.300 31.623 scale StandardScaler() 0 -> 0.5494465709966122
 2508/3780 0.300 31.623 scale MinMaxScaler()   0 -> 0.5207185458903869
 2509/3780 0.300 31.623 auto StandardScaler() 0 -> 0.549446570996611
 2510/3780 0.300 31.623 auto MinMaxScaler()   0 -> 0.4970537014523099
 2511/3780 0.300 31.623 0.01 StandardScaler() 0 -> 0.4871712029041491
 2512/3780 0.300 31.623 0.01 MinMaxScaler()   0 -> 0.5438690411826638
 2513/3780 0.300 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4849459903217183
 2514/3780 0.300 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5177071497758486
 2515/3780 0.300 31.623 0.1 StandardScaler() 0 -> 0.5614386529582956
 2516/3780 0.300 31.623 0.1 MinMaxScaler()   0 -> 0.4959070174661633
 2517/3780 0.300 31.623 0.31622776601683794 StandardScaler() 0 -> 0.505743860095687
 2518/3780 0.300 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4857667368364209
 2519/3780 0.300 31.623 1.0 StandardScaler() 0 -> 0.5017410981188717
 2520/3780 0.300 31.623 1.0 MinMaxScaler()   0 -> 0.4829850131311355
 2521/3780 0.350 0.032 scale StandardScaler() 0 -> 0.5351982855846693
 2522/3780 0.350 0.032 scale MinMaxScaler()   0 -> 0.53177759652953
 2523/3780 0.350 0.032 auto StandardScaler() 0 -> 0.5351982855846693
 2524/3780 0.350 0.032 auto MinMaxScaler()   0 -> 0.6358066677393087
 2525/3780 0.350 0.032 0.01 StandardScaler() 0 -> 0.5808957754380176
 2526/3780 0.350 0.032 0.01 MinMaxScaler()   0 -> 0.7946676640726226
 2527/3780 0.350 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5431308376852377
 2528/3780 0.350 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7374243834120708
 2529/3780 0.350 0.032 0.1 StandardScaler() 0 -> 0.5368521397717742
 2530/3780 0.350 0.032 0.1 MinMaxScaler()   0 -> 0.6269109274770139
 2531/3780 0.350 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6124787813413731
 2532/3780 0.350 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5699198293583766
 2533/3780 0.350 0.032 1.0 StandardScaler() 0 -> 0.794533337443765
 2534/3780 0.350 0.032 1.0 MinMaxScaler()   0 -> 0.537578669860949
 2535/3780 0.350 0.040 scale StandardScaler() 0 -> 0.5294483797177529
 2536/3780 0.350 0.040 scale MinMaxScaler()   0 -> 0.5257611931831389
 2537/3780 0.350 0.040 auto StandardScaler() 0 -> 0.5294483797177528
 2538/3780 0.350 0.040 auto MinMaxScaler()   0 -> 0.6154335862795959
 2539/3780 0.350 0.040 0.01 StandardScaler() 0 -> 0.5715926500198552
 2540/3780 0.350 0.040 0.01 MinMaxScaler()   0 -> 0.7867995311998913
 2541/3780 0.350 0.040 0.03162277660168379 StandardScaler() 0 -> 0.537176109876755
 2542/3780 0.350 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7175338939297844
 2543/3780 0.350 0.040 0.1 StandardScaler() 0 -> 0.5303365078717593
 2544/3780 0.350 0.040 0.1 MinMaxScaler()   0 -> 0.6092253057113403
 2545/3780 0.350 0.040 0.31622776601683794 StandardScaler() 0 -> 0.595525645762344
 2546/3780 0.350 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5617157778555341
 2547/3780 0.350 0.040 1.0 StandardScaler() 0 -> 0.7868073715632072
 2548/3780 0.350 0.040 1.0 MinMaxScaler()   0 -> 0.5323984650282858
 2549/3780 0.350 0.051 scale StandardScaler() 0 -> 0.5220039983324075
 2550/3780 0.350 0.051 scale MinMaxScaler()   0 -> 0.5200389890397468
 2551/3780 0.350 0.051 auto StandardScaler() 0 -> 0.5220039983324075
 2552/3780 0.350 0.051 auto MinMaxScaler()   0 -> 0.6004775886156343
 2553/3780 0.350 0.051 0.01 StandardScaler() 0 -> 0.5631864974049821
 2554/3780 0.350 0.051 0.01 MinMaxScaler()   0 -> 0.7770972840487581
 2555/3780 0.350 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5309622555241476
 2556/3780 0.350 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.6945301368782072
 2557/3780 0.350 0.051 0.1 StandardScaler() 0 -> 0.5226500431038721
 2558/3780 0.350 0.051 0.1 MinMaxScaler()   0 -> 0.5958481261480143
 2559/3780 0.350 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5796507771581777
 2560/3780 0.350 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5554496126945452
 2561/3780 0.350 0.051 1.0 StandardScaler() 0 -> 0.7773791212616183
 2562/3780 0.350 0.051 1.0 MinMaxScaler()   0 -> 0.5269591743010525
 2563/3780 0.350 0.065 scale StandardScaler() 0 -> 0.5149096072025282
 2564/3780 0.350 0.065 scale MinMaxScaler()   0 -> 0.5147943432606039
 2565/3780 0.350 0.065 auto StandardScaler() 0 -> 0.5149096072025282
 2566/3780 0.350 0.065 auto MinMaxScaler()   0 -> 0.5897400157204916
 2567/3780 0.350 0.065 0.01 StandardScaler() 0 -> 0.5560198412678444
 2568/3780 0.350 0.065 0.01 MinMaxScaler()   0 -> 0.7651603159834903
 2569/3780 0.350 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5250810891808881
 2570/3780 0.350 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6689361314125207
 2571/3780 0.350 0.065 0.1 StandardScaler() 0 -> 0.5151371936382353
 2572/3780 0.350 0.065 0.1 MinMaxScaler()   0 -> 0.5858934171052202
 2573/3780 0.350 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5649225541996916
 2574/3780 0.350 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5497781455493108
 2575/3780 0.350 0.065 1.0 StandardScaler() 0 -> 0.7659381792775167
 2576/3780 0.350 0.065 1.0 MinMaxScaler()   0 -> 0.5217447174090631
 2577/3780 0.350 0.082 scale StandardScaler() 0 -> 0.508711618053313
 2578/3780 0.350 0.082 scale MinMaxScaler()   0 -> 0.5089454581279914
 2579/3780 0.350 0.082 auto StandardScaler() 0 -> 0.508711618053313
 2580/3780 0.350 0.082 auto MinMaxScaler()   0 -> 0.5811434322495712
 2581/3780 0.350 0.082 0.01 StandardScaler() 0 -> 0.5499212667955194
 2582/3780 0.350 0.082 0.01 MinMaxScaler()   0 -> 0.750616303614216
 2583/3780 0.350 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5190190346506812
 2584/3780 0.350 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6427809351989112
 2585/3780 0.350 0.082 0.1 StandardScaler() 0 -> 0.50921608174797
 2586/3780 0.350 0.082 0.1 MinMaxScaler()   0 -> 0.5777690634696815
 2587/3780 0.350 0.082 0.31622776601683794 StandardScaler() 0 -> 0.550414370215437
 2588/3780 0.350 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5444888947648457
 2589/3780 0.350 0.082 1.0 StandardScaler() 0 -> 0.7520088606160233
 2590/3780 0.350 0.082 1.0 MinMaxScaler()   0 -> 0.5173007533432795
 2591/3780 0.350 0.104 scale StandardScaler() 0 -> 0.5030939564315693
 2592/3780 0.350 0.104 scale MinMaxScaler()   0 -> 0.5037105063010285
 2593/3780 0.350 0.104 auto StandardScaler() 0 -> 0.5030939564315693
 2594/3780 0.350 0.104 auto MinMaxScaler()   0 -> 0.5725935527267456
 2595/3780 0.350 0.104 0.01 StandardScaler() 0 -> 0.5448951418098987
 2596/3780 0.350 0.104 0.01 MinMaxScaler()   0 -> 0.7331390608430137
 2597/3780 0.350 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5141907943850399
 2598/3780 0.350 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6214248917457498
 2599/3780 0.350 0.104 0.1 StandardScaler() 0 -> 0.5038172623921914
 2600/3780 0.350 0.104 0.1 MinMaxScaler()   0 -> 0.5695386453426616
 2601/3780 0.350 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5370925376318111
 2602/3780 0.350 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5399570399241896
 2603/3780 0.350 0.104 1.0 StandardScaler() 0 -> 0.7349908721816223
 2604/3780 0.350 0.104 1.0 MinMaxScaler()   0 -> 0.5129864601233699
 2605/3780 0.350 0.132 scale StandardScaler() 0 -> 0.498744912589125
 2606/3780 0.350 0.132 scale MinMaxScaler()   0 -> 0.49902090171023666
 2607/3780 0.350 0.132 auto StandardScaler() 0 -> 0.498744912589125
 2608/3780 0.350 0.132 auto MinMaxScaler()   0 -> 0.5661148632002883
 2609/3780 0.350 0.132 0.01 StandardScaler() 0 -> 0.5395795696942056
 2610/3780 0.350 0.132 0.01 MinMaxScaler()   0 -> 0.7124931908494565
 2611/3780 0.350 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5088983629857924
 2612/3780 0.350 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6052150577184842
 2613/3780 0.350 0.132 0.1 StandardScaler() 0 -> 0.49875392532979057
 2614/3780 0.350 0.132 0.1 MinMaxScaler()   0 -> 0.5637664665205419
 2615/3780 0.350 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5261995874338089
 2616/3780 0.350 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5354215801919819
 2617/3780 0.350 0.132 1.0 StandardScaler() 0 -> 0.7139873704642435
 2618/3780 0.350 0.132 1.0 MinMaxScaler()   0 -> 0.5085978925167005
 2619/3780 0.350 0.168 scale StandardScaler() 0 -> 0.4945736860631483
 2620/3780 0.350 0.168 scale MinMaxScaler()   0 -> 0.49516590576572234
 2621/3780 0.350 0.168 auto StandardScaler() 0 -> 0.4945736860631484
 2622/3780 0.350 0.168 auto MinMaxScaler()   0 -> 0.5618044262089111
 2623/3780 0.350 0.168 0.01 StandardScaler() 0 -> 0.5348770881593925
 2624/3780 0.350 0.168 0.01 MinMaxScaler()   0 -> 0.6888409421178435
 2625/3780 0.350 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5055151712397269
 2626/3780 0.350 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.5936132674266742
 2627/3780 0.350 0.168 0.1 StandardScaler() 0 -> 0.49458725281906685
 2628/3780 0.350 0.168 0.1 MinMaxScaler()   0 -> 0.5597753461387642
 2629/3780 0.350 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5164734456458652
 2630/3780 0.350 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5309644735837599
 2631/3780 0.350 0.168 1.0 StandardScaler() 0 -> 0.6879115127722605
 2632/3780 0.350 0.168 1.0 MinMaxScaler()   0 -> 0.5045750811656095
 2633/3780 0.350 0.213 scale StandardScaler() 0 -> 0.49075788926013386
 2634/3780 0.350 0.213 scale MinMaxScaler()   0 -> 0.49170118287641845
 2635/3780 0.350 0.213 auto StandardScaler() 0 -> 0.4907578892601339
 2636/3780 0.350 0.213 auto MinMaxScaler()   0 -> 0.5572286300025732
 2637/3780 0.350 0.213 0.01 StandardScaler() 0 -> 0.5302755569614015
 2638/3780 0.350 0.213 0.01 MinMaxScaler()   0 -> 0.662899358600249
 2639/3780 0.350 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5012089435857757
 2640/3780 0.350 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5848973602123403
 2641/3780 0.350 0.213 0.1 StandardScaler() 0 -> 0.49047848203526767
 2642/3780 0.350 0.213 0.1 MinMaxScaler()   0 -> 0.5551190564568325
 2643/3780 0.350 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5072704955845653
 2644/3780 0.350 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5270953950979065
 2645/3780 0.350 0.213 1.0 StandardScaler() 0 -> 0.6577616514305226
 2646/3780 0.350 0.213 1.0 MinMaxScaler()   0 -> 0.5004773824532339
 2647/3780 0.350 0.270 scale StandardScaler() 0 -> 0.4873308067289172
 2648/3780 0.350 0.270 scale MinMaxScaler()   0 -> 0.4890173516261018
 2649/3780 0.350 0.270 auto StandardScaler() 0 -> 0.4873308067289172
 2650/3780 0.350 0.270 auto MinMaxScaler()   0 -> 0.5536352166826007
 2651/3780 0.350 0.270 0.01 StandardScaler() 0 -> 0.5259657324795213
 2652/3780 0.350 0.270 0.01 MinMaxScaler()   0 -> 0.6376077713476067
 2653/3780 0.350 0.270 0.03162277660168379 StandardScaler() 0 -> 0.49815336391950743
 2654/3780 0.350 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5768155917205804
 2655/3780 0.350 0.270 0.1 StandardScaler() 0 -> 0.487093197452466
 2656/3780 0.350 0.270 0.1 MinMaxScaler()   0 -> 0.551944943357364
 2657/3780 0.350 0.270 0.31622776601683794 StandardScaler() 0 -> 0.4988104216774823
 2658/3780 0.350 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5237051024543526
 2659/3780 0.350 0.270 1.0 StandardScaler() 0 -> 0.6258390429531717
 2660/3780 0.350 0.270 1.0 MinMaxScaler()   0 -> 0.4978079058570808
 2661/3780 0.350 0.342 scale StandardScaler() 0 -> 0.48420262801374814
 2662/3780 0.350 0.342 scale MinMaxScaler()   0 -> 0.4859175741075194
 2663/3780 0.350 0.342 auto StandardScaler() 0 -> 0.4842026280137483
 2664/3780 0.350 0.342 auto MinMaxScaler()   0 -> 0.550231624965951
 2665/3780 0.350 0.342 0.01 StandardScaler() 0 -> 0.5221255676664168
 2666/3780 0.350 0.342 0.01 MinMaxScaler()   0 -> 0.6172998549727499
 2667/3780 0.350 0.342 0.03162277660168379 StandardScaler() 0 -> 0.49445927450495036
 2668/3780 0.350 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5699302042306045
 2669/3780 0.350 0.342 0.1 StandardScaler() 0 -> 0.48341537528246437
 2670/3780 0.350 0.342 0.1 MinMaxScaler()   0 -> 0.5484994721943366
 2671/3780 0.350 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4908731419372209
 2672/3780 0.350 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5204674772294577
 2673/3780 0.350 0.342 1.0 StandardScaler() 0 -> 0.5934908493286923
 2674/3780 0.350 0.342 1.0 MinMaxScaler()   0 -> 0.4947948042927924
 2675/3780 0.350 0.434 scale StandardScaler() 0 -> 0.4812359021702433
 2676/3780 0.350 0.434 scale MinMaxScaler()   0 -> 0.48349455020533466
 2677/3780 0.350 0.434 auto StandardScaler() 0 -> 0.48123590217024326
 2678/3780 0.350 0.434 auto MinMaxScaler()   0 -> 0.5473225689675124
 2679/3780 0.350 0.434 0.01 StandardScaler() 0 -> 0.5180645497315463
 2680/3780 0.350 0.434 0.01 MinMaxScaler()   0 -> 0.6028639355718077
 2681/3780 0.350 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49151942878092286
 2682/3780 0.350 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5653632367012699
 2683/3780 0.350 0.434 0.1 StandardScaler() 0 -> 0.4804745580623366
 2684/3780 0.350 0.434 0.1 MinMaxScaler()   0 -> 0.5452249079276825
 2685/3780 0.350 0.434 0.31622776601683794 StandardScaler() 0 -> 0.48393206232034186
 2686/3780 0.350 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5162556641336208
 2687/3780 0.350 0.434 1.0 StandardScaler() 0 -> 0.5644526287628068
 2688/3780 0.350 0.434 1.0 MinMaxScaler()   0 -> 0.4925346875823025
 2689/3780 0.350 0.551 scale StandardScaler() 0 -> 0.4788085891098328
 2690/3780 0.350 0.551 scale MinMaxScaler()   0 -> 0.48064765885590094
 2691/3780 0.350 0.551 auto StandardScaler() 0 -> 0.47880858910983265
 2692/3780 0.350 0.551 auto MinMaxScaler()   0 -> 0.5441173255571425
 2693/3780 0.350 0.551 0.01 StandardScaler() 0 -> 0.5144430455261705
 2694/3780 0.350 0.551 0.01 MinMaxScaler()   0 -> 0.5918094816779802
 2695/3780 0.350 0.551 0.03162277660168379 StandardScaler() 0 -> 0.4899128295035677
 2696/3780 0.350 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5613576050692541
 2697/3780 0.350 0.551 0.1 StandardScaler() 0 -> 0.47814014145759004
 2698/3780 0.350 0.551 0.1 MinMaxScaler()   0 -> 0.5420484729453182
 2699/3780 0.350 0.551 0.31622776601683794 StandardScaler() 0 -> 0.477839240292905
 2700/3780 0.350 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5124831848989873
 2701/3780 0.350 0.551 1.0 StandardScaler() 0 -> 0.5417582060181861
 2702/3780 0.350 0.551 1.0 MinMaxScaler()   0 -> 0.49050238177689187
 2703/3780 0.350 0.700 scale StandardScaler() 0 -> 0.4767842692078146
 2704/3780 0.350 0.700 scale MinMaxScaler()   0 -> 0.4783274410089409
 2705/3780 0.350 0.700 auto StandardScaler() 0 -> 0.4767842692078144
 2706/3780 0.350 0.700 auto MinMaxScaler()   0 -> 0.5409037417840074
 2707/3780 0.350 0.700 0.01 StandardScaler() 0 -> 0.5110605713494497
 2708/3780 0.350 0.700 0.01 MinMaxScaler()   0 -> 0.5835102563203644
 2709/3780 0.350 0.700 0.03162277660168379 StandardScaler() 0 -> 0.48834460610596314
 2710/3780 0.350 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5579185194994486
 2711/3780 0.350 0.700 0.1 StandardScaler() 0 -> 0.4760935832193994
 2712/3780 0.350 0.700 0.1 MinMaxScaler()   0 -> 0.5385713348950608
 2713/3780 0.350 0.700 0.31622776601683794 StandardScaler() 0 -> 0.47271060651881297
 2714/3780 0.350 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5091851515470226
 2715/3780 0.350 0.700 1.0 StandardScaler() 0 -> 0.5268580025908157
 2716/3780 0.350 0.700 1.0 MinMaxScaler()   0 -> 0.4887163733606275
 2717/3780 0.350 0.888 scale StandardScaler() 0 -> 0.47508983303540386
 2718/3780 0.350 0.888 scale MinMaxScaler()   0 -> 0.4760469487528316
 2719/3780 0.350 0.888 auto StandardScaler() 0 -> 0.4750898330354039
 2720/3780 0.350 0.888 auto MinMaxScaler()   0 -> 0.5375172218106532
 2721/3780 0.350 0.888 0.01 StandardScaler() 0 -> 0.5077917685884785
 2722/3780 0.350 0.888 0.01 MinMaxScaler()   0 -> 0.575650858919951
 2723/3780 0.350 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4870169512775509
 2724/3780 0.350 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5557819121948667
 2725/3780 0.350 0.888 0.1 StandardScaler() 0 -> 0.4740827225792741
 2726/3780 0.350 0.888 0.1 MinMaxScaler()   0 -> 0.5350476750420422
 2727/3780 0.350 0.888 0.31622776601683794 StandardScaler() 0 -> 0.4693286737797642
 2728/3780 0.350 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5064469669278796
 2729/3780 0.350 0.888 1.0 StandardScaler() 0 -> 0.5193237595053688
 2730/3780 0.350 0.888 1.0 MinMaxScaler()   0 -> 0.4869562110003504
 2731/3780 0.350 1.126 scale StandardScaler() 0 -> 0.47296226028671695
 2732/3780 0.350 1.126 scale MinMaxScaler()   0 -> 0.4739631612167832
 2733/3780 0.350 1.126 auto StandardScaler() 0 -> 0.4729622602867171
 2734/3780 0.350 1.126 auto MinMaxScaler()   0 -> 0.534189658494767
 2735/3780 0.350 1.126 0.01 StandardScaler() 0 -> 0.5045451095382634
 2736/3780 0.350 1.126 0.01 MinMaxScaler()   0 -> 0.569425479231403
 2737/3780 0.350 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48551494798496786
 2738/3780 0.350 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.553572109717602
 2739/3780 0.350 1.126 0.1 StandardScaler() 0 -> 0.47144155257342774
 2740/3780 0.350 1.126 0.1 MinMaxScaler()   0 -> 0.5321623542174375
 2741/3780 0.350 1.126 0.31622776601683794 StandardScaler() 0 -> 0.467061593260984
 2742/3780 0.350 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5038284151044702
 2743/3780 0.350 1.126 1.0 StandardScaler() 0 -> 0.5140653731367811
 2744/3780 0.350 1.126 1.0 MinMaxScaler()   0 -> 0.48482583058255996
 2745/3780 0.350 1.429 scale StandardScaler() 0 -> 0.47077867895459596
 2746/3780 0.350 1.429 scale MinMaxScaler()   0 -> 0.47308250265692003
 2747/3780 0.350 1.429 auto StandardScaler() 0 -> 0.4707786789545958
 2748/3780 0.350 1.429 auto MinMaxScaler()   0 -> 0.5314721344557798
 2749/3780 0.350 1.429 0.01 StandardScaler() 0 -> 0.5014017686263682
 2750/3780 0.350 1.429 0.01 MinMaxScaler()   0 -> 0.5654975319910936
 2751/3780 0.350 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48409087263383555
 2752/3780 0.350 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5515653977499339
 2753/3780 0.350 1.429 0.1 StandardScaler() 0 -> 0.4695697694655405
 2754/3780 0.350 1.429 0.1 MinMaxScaler()   0 -> 0.5291869879216583
 2755/3780 0.350 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4658897425759378
 2756/3780 0.350 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5011066140662073
 2757/3780 0.350 1.429 1.0 StandardScaler() 0 -> 0.51001099315551
 2758/3780 0.350 1.429 1.0 MinMaxScaler()   0 -> 0.4831971684635421
 2759/3780 0.350 1.814 scale StandardScaler() 0 -> 0.4695487859606864
 2760/3780 0.350 1.814 scale MinMaxScaler()   0 -> 0.47271135769730854
 2761/3780 0.350 1.814 auto StandardScaler() 0 -> 0.4695487859606868
 2762/3780 0.350 1.814 auto MinMaxScaler()   0 -> 0.5286208093347646
 2763/3780 0.350 1.814 0.01 StandardScaler() 0 -> 0.4987270759361664
 2764/3780 0.350 1.814 0.01 MinMaxScaler()   0 -> 0.5617245101933471
 2765/3780 0.350 1.814 0.03162277660168379 StandardScaler() 0 -> 0.4829324776755071
 2766/3780 0.350 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5493798847252822
 2767/3780 0.350 1.814 0.1 StandardScaler() 0 -> 0.46879068988009304
 2768/3780 0.350 1.814 0.1 MinMaxScaler()   0 -> 0.5260964188709362
 2769/3780 0.350 1.814 0.31622776601683794 StandardScaler() 0 -> 0.46706154018318924
 2770/3780 0.350 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.49816312288723624
 2771/3780 0.350 1.814 1.0 StandardScaler() 0 -> 0.508442122073269
 2772/3780 0.350 1.814 1.0 MinMaxScaler()   0 -> 0.48178136237356206
 2773/3780 0.350 2.302 scale StandardScaler() 0 -> 0.46911304537577075
 2774/3780 0.350 2.302 scale MinMaxScaler()   0 -> 0.4722587907508704
 2775/3780 0.350 2.302 auto StandardScaler() 0 -> 0.46911304537577075
 2776/3780 0.350 2.302 auto MinMaxScaler()   0 -> 0.5255878091399454
 2777/3780 0.350 2.302 0.01 StandardScaler() 0 -> 0.49656228550943965
 2778/3780 0.350 2.302 0.01 MinMaxScaler()   0 -> 0.5589104036612048
 2779/3780 0.350 2.302 0.03162277660168379 StandardScaler() 0 -> 0.48193164194125765
 2780/3780 0.350 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5476527903426462
 2781/3780 0.350 2.302 0.1 StandardScaler() 0 -> 0.46874608995591566
 2782/3780 0.350 2.302 0.1 MinMaxScaler()   0 -> 0.5230912934271015
 2783/3780 0.350 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4694577215422999
 2784/3780 0.350 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.49615519445452705
 2785/3780 0.350 2.302 1.0 StandardScaler() 0 -> 0.5081590728829629
 2786/3780 0.350 2.302 1.0 MinMaxScaler()   0 -> 0.48029772646703045
 2787/3780 0.350 2.921 scale StandardScaler() 0 -> 0.46951953344880565
 2788/3780 0.350 2.921 scale MinMaxScaler()   0 -> 0.47194196605998245
 2789/3780 0.350 2.921 auto StandardScaler() 0 -> 0.4695195334488071
 2790/3780 0.350 2.921 auto MinMaxScaler()   0 -> 0.5222882497884779
 2791/3780 0.350 2.921 0.01 StandardScaler() 0 -> 0.49563481723169334
 2792/3780 0.350 2.921 0.01 MinMaxScaler()   0 -> 0.557284346468589
 2793/3780 0.350 2.921 0.03162277660168379 StandardScaler() 0 -> 0.4811150024053968
 2794/3780 0.350 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5456801962910366
 2795/3780 0.350 2.921 0.1 StandardScaler() 0 -> 0.46992379538851187
 2796/3780 0.350 2.921 0.1 MinMaxScaler()   0 -> 0.5194669139249966
 2797/3780 0.350 2.921 0.31622776601683794 StandardScaler() 0 -> 0.4737001906615501
 2798/3780 0.350 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49430584983160336
 2799/3780 0.350 2.921 1.0 StandardScaler() 0 -> 0.5080762046573595
 2800/3780 0.350 2.921 1.0 MinMaxScaler()   0 -> 0.4792894863262054
 2801/3780 0.350 3.707 scale StandardScaler() 0 -> 0.47100326744940596
 2802/3780 0.350 3.707 scale MinMaxScaler()   0 -> 0.4724494121215601
 2803/3780 0.350 3.707 auto StandardScaler() 0 -> 0.471003267449406
 2804/3780 0.350 3.707 auto MinMaxScaler()   0 -> 0.5188117125474173
 2805/3780 0.350 3.707 0.01 StandardScaler() 0 -> 0.4939904476872273
 2806/3780 0.350 3.707 0.01 MinMaxScaler()   0 -> 0.5557395008742133
 2807/3780 0.350 3.707 0.03162277660168379 StandardScaler() 0 -> 0.48010020328927366
 2808/3780 0.350 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5431876497743855
 2809/3780 0.350 3.707 0.1 StandardScaler() 0 -> 0.4711785370955864
 2810/3780 0.350 3.707 0.1 MinMaxScaler()   0 -> 0.5162233314310026
 2811/3780 0.350 3.707 0.31622776601683794 StandardScaler() 0 -> 0.47837548580029976
 2812/3780 0.350 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4927305315033316
 2813/3780 0.350 3.707 1.0 StandardScaler() 0 -> 0.508058454161724
 2814/3780 0.350 3.707 1.0 MinMaxScaler()   0 -> 0.4777247360925834
 2815/3780 0.350 4.703 scale StandardScaler() 0 -> 0.4727713791647488
 2816/3780 0.350 4.703 scale MinMaxScaler()   0 -> 0.47336014171836976
 2817/3780 0.350 4.703 auto StandardScaler() 0 -> 0.47277137916474826
 2818/3780 0.350 4.703 auto MinMaxScaler()   0 -> 0.5155621756438379
 2819/3780 0.350 4.703 0.01 StandardScaler() 0 -> 0.49304717622152944
 2820/3780 0.350 4.703 0.01 MinMaxScaler()   0 -> 0.5541565912051668
 2821/3780 0.350 4.703 0.03162277660168379 StandardScaler() 0 -> 0.4796523731292202
 2822/3780 0.350 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5403926394094335
 2823/3780 0.350 4.703 0.1 StandardScaler() 0 -> 0.47290880971189414
 2824/3780 0.350 4.703 0.1 MinMaxScaler()   0 -> 0.5131552110561837
 2825/3780 0.350 4.703 0.31622776601683794 StandardScaler() 0 -> 0.4831148659353571
 2826/3780 0.350 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.4920585685600034
 2827/3780 0.350 4.703 1.0 StandardScaler() 0 -> 0.508058337123614
 2828/3780 0.350 4.703 1.0 MinMaxScaler()   0 -> 0.4768115157547965
 2829/3780 0.350 5.968 scale StandardScaler() 0 -> 0.47488279026155
 2830/3780 0.350 5.968 scale MinMaxScaler()   0 -> 0.47495911604647345
 2831/3780 0.350 5.968 auto StandardScaler() 0 -> 0.47488279026154984
 2832/3780 0.350 5.968 auto MinMaxScaler()   0 -> 0.5127650181962113
 2833/3780 0.350 5.968 0.01 StandardScaler() 0 -> 0.49233670254705125
 2834/3780 0.350 5.968 0.01 MinMaxScaler()   0 -> 0.5528463184623486
 2835/3780 0.350 5.968 0.03162277660168379 StandardScaler() 0 -> 0.4795995778413437
 2836/3780 0.350 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.537986818086571
 2837/3780 0.350 5.968 0.1 StandardScaler() 0 -> 0.4765766666858491
 2838/3780 0.350 5.968 0.1 MinMaxScaler()   0 -> 0.5104724678031974
 2839/3780 0.350 5.968 0.31622776601683794 StandardScaler() 0 -> 0.48754612955877485
 2840/3780 0.350 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.4915652172941056
 2841/3780 0.350 5.968 1.0 StandardScaler() 0 -> 0.5080589534805994
 2842/3780 0.350 5.968 1.0 MinMaxScaler()   0 -> 0.4765037259222417
 2843/3780 0.350 7.574 scale StandardScaler() 0 -> 0.47876188450152113
 2844/3780 0.350 7.574 scale MinMaxScaler()   0 -> 0.47764128293559777
 2845/3780 0.350 7.574 auto StandardScaler() 0 -> 0.4787618845015205
 2846/3780 0.350 7.574 auto MinMaxScaler()   0 -> 0.5100652955005339
 2847/3780 0.350 7.574 0.01 StandardScaler() 0 -> 0.49151659218746707
 2848/3780 0.350 7.574 0.01 MinMaxScaler()   0 -> 0.5517294631551718
 2849/3780 0.350 7.574 0.03162277660168379 StandardScaler() 0 -> 0.47889514205234773
 2850/3780 0.350 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5355707360709788
 2851/3780 0.350 7.574 0.1 StandardScaler() 0 -> 0.48193116973633937
 2852/3780 0.350 7.574 0.1 MinMaxScaler()   0 -> 0.5082394613258386
 2853/3780 0.350 7.574 0.31622776601683794 StandardScaler() 0 -> 0.49154217778298653
 2854/3780 0.350 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.4909595746518545
 2855/3780 0.350 7.574 1.0 StandardScaler() 0 -> 0.5080589534805994
 2856/3780 0.350 7.574 1.0 MinMaxScaler()   0 -> 0.47630811083425195
 2857/3780 0.350 9.611 scale StandardScaler() 0 -> 0.4842329621303922
 2858/3780 0.350 9.611 scale MinMaxScaler()   0 -> 0.4803907781230868
 2859/3780 0.350 9.611 auto StandardScaler() 0 -> 0.48423296213039174
 2860/3780 0.350 9.611 auto MinMaxScaler()   0 -> 0.5077185887902639
 2861/3780 0.350 9.611 0.01 StandardScaler() 0 -> 0.490811724442703
 2862/3780 0.350 9.611 0.01 MinMaxScaler()   0 -> 0.5506274700404878
 2863/3780 0.350 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4778319175705394
 2864/3780 0.350 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5332983846083001
 2865/3780 0.350 9.611 0.1 StandardScaler() 0 -> 0.490023946002155
 2866/3780 0.350 9.611 0.1 MinMaxScaler()   0 -> 0.5056491672309078
 2867/3780 0.350 9.611 0.31622776601683794 StandardScaler() 0 -> 0.4952854012572676
 2868/3780 0.350 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4900246612443337
 2869/3780 0.350 9.611 1.0 StandardScaler() 0 -> 0.5080589534805994
 2870/3780 0.350 9.611 1.0 MinMaxScaler()   0 -> 0.4761712592678892
 2871/3780 0.350 12.196 scale StandardScaler() 0 -> 0.4927921044223553
 2872/3780 0.350 12.196 scale MinMaxScaler()   0 -> 0.4840687819028404
 2873/3780 0.350 12.196 auto StandardScaler() 0 -> 0.49279210442235355
 2874/3780 0.350 12.196 auto MinMaxScaler()   0 -> 0.5051952121475657
 2875/3780 0.350 12.196 0.01 StandardScaler() 0 -> 0.4899416735826428
 2876/3780 0.350 12.196 0.01 MinMaxScaler()   0 -> 0.5494276383473631
 2877/3780 0.350 12.196 0.03162277660168379 StandardScaler() 0 -> 0.47742984106458763
 2878/3780 0.350 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5304173760879375
 2879/3780 0.350 12.196 0.1 StandardScaler() 0 -> 0.49825948247681606
 2880/3780 0.350 12.196 0.1 MinMaxScaler()   0 -> 0.5036479653104715
 2881/3780 0.350 12.196 0.31622776601683794 StandardScaler() 0 -> 0.49780856150728536
 2882/3780 0.350 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4891955295810877
 2883/3780 0.350 12.196 1.0 StandardScaler() 0 -> 0.5080589534805994
 2884/3780 0.350 12.196 1.0 MinMaxScaler()   0 -> 0.4765135884085589
 2885/3780 0.350 15.476 scale StandardScaler() 0 -> 0.5023715382194966
 2886/3780 0.350 15.476 scale MinMaxScaler()   0 -> 0.4899385009900663
 2887/3780 0.350 15.476 auto StandardScaler() 0 -> 0.5023715382195014
 2888/3780 0.350 15.476 auto MinMaxScaler()   0 -> 0.5031044145653891
 2889/3780 0.350 15.476 0.01 StandardScaler() 0 -> 0.4891474275692287
 2890/3780 0.350 15.476 0.01 MinMaxScaler()   0 -> 0.5481393753472695
 2891/3780 0.350 15.476 0.03162277660168379 StandardScaler() 0 -> 0.4783126352838882
 2892/3780 0.350 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5273993124879058
 2893/3780 0.350 15.476 0.1 StandardScaler() 0 -> 0.509232142853744
 2894/3780 0.350 15.476 0.1 MinMaxScaler()   0 -> 0.5011681157827043
 2895/3780 0.350 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5000102003147513
 2896/3780 0.350 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.48818154338100905
 2897/3780 0.350 15.476 1.0 StandardScaler() 0 -> 0.5080589534805994
 2898/3780 0.350 15.476 1.0 MinMaxScaler()   0 -> 0.4773943916267389
 2899/3780 0.350 19.638 scale StandardScaler() 0 -> 0.5135539925488356
 2900/3780 0.350 19.638 scale MinMaxScaler()   0 -> 0.49773758204911195
 2901/3780 0.350 19.638 auto StandardScaler() 0 -> 0.5135539925488337
 2902/3780 0.350 19.638 auto MinMaxScaler()   0 -> 0.5009407856771265
 2903/3780 0.350 19.638 0.01 StandardScaler() 0 -> 0.48841517324477607
 2904/3780 0.350 19.638 0.01 MinMaxScaler()   0 -> 0.5466266859688922
 2905/3780 0.350 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4798494176759191
 2906/3780 0.350 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5247125411394472
 2907/3780 0.350 19.638 0.1 StandardScaler() 0 -> 0.5229304226564903
 2908/3780 0.350 19.638 0.1 MinMaxScaler()   0 -> 0.49904043354944033
 2909/3780 0.350 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5016427868034175
 2910/3780 0.350 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.4875699569429166
 2911/3780 0.350 19.638 1.0 StandardScaler() 0 -> 0.5080589534805994
 2912/3780 0.350 19.638 1.0 MinMaxScaler()   0 -> 0.47867457379806705
 2913/3780 0.350 24.920 scale StandardScaler() 0 -> 0.5284888137053309
 2914/3780 0.350 24.920 scale MinMaxScaler()   0 -> 0.5072315350035735
 2915/3780 0.350 24.920 auto StandardScaler() 0 -> 0.5284888137053232
 2916/3780 0.350 24.920 auto MinMaxScaler()   0 -> 0.49883308719640745
 2917/3780 0.350 24.920 0.01 StandardScaler() 0 -> 0.48764021043155886
 2918/3780 0.350 24.920 0.01 MinMaxScaler()   0 -> 0.5451416529246774
 2919/3780 0.350 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4815759342124155
 2920/3780 0.350 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5214116740382545
 2921/3780 0.350 24.920 0.1 StandardScaler() 0 -> 0.5365673785158669
 2922/3780 0.350 24.920 0.1 MinMaxScaler()   0 -> 0.4973128841615497
 2923/3780 0.350 24.920 0.31622776601683794 StandardScaler() 0 -> 0.502000195118326
 2924/3780 0.350 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.48626270251354836
 2925/3780 0.350 24.920 1.0 StandardScaler() 0 -> 0.5080589534805994
 2926/3780 0.350 24.920 1.0 MinMaxScaler()   0 -> 0.4800230231507386
 2927/3780 0.350 31.623 scale StandardScaler() 0 -> 0.5435223745110555
 2928/3780 0.350 31.623 scale MinMaxScaler()   0 -> 0.517684611681737
 2929/3780 0.350 31.623 auto StandardScaler() 0 -> 0.543522374511058
 2930/3780 0.350 31.623 auto MinMaxScaler()   0 -> 0.49720406213649554
 2931/3780 0.350 31.623 0.01 StandardScaler() 0 -> 0.48691261519619644
 2932/3780 0.350 31.623 0.01 MinMaxScaler()   0 -> 0.5434704707962669
 2933/3780 0.350 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4830040150574089
 2934/3780 0.350 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.517880280727327
 2935/3780 0.350 31.623 0.1 StandardScaler() 0 -> 0.5535284120671519
 2936/3780 0.350 31.623 0.1 MinMaxScaler()   0 -> 0.49603812573609396
 2937/3780 0.350 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5022568815784573
 2938/3780 0.350 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.48505631491865603
 2939/3780 0.350 31.623 1.0 StandardScaler() 0 -> 0.5080589534805994
 2940/3780 0.350 31.623 1.0 MinMaxScaler()   0 -> 0.48245105786920967
 2941/3780 0.400 0.032 scale StandardScaler() 0 -> 0.5417348681242031
 2942/3780 0.400 0.032 scale MinMaxScaler()   0 -> 0.5370060659616941
 2943/3780 0.400 0.032 auto StandardScaler() 0 -> 0.5417348681242031
 2944/3780 0.400 0.032 auto MinMaxScaler()   0 -> 0.6475939493873989
 2945/3780 0.400 0.032 0.01 StandardScaler() 0 -> 0.581873283256096
 2946/3780 0.400 0.032 0.01 MinMaxScaler()   0 -> 0.8213766959077392
 2947/3780 0.400 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5478079719280933
 2948/3780 0.400 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.7598602997195522
 2949/3780 0.400 0.032 0.1 StandardScaler() 0 -> 0.5428785411404848
 2950/3780 0.400 0.032 0.1 MinMaxScaler()   0 -> 0.637029655201282
 2951/3780 0.400 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6219385891786092
 2952/3780 0.400 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5732590240568417
 2953/3780 0.400 0.032 1.0 StandardScaler() 0 -> 0.8236325892790992
 2954/3780 0.400 0.032 1.0 MinMaxScaler()   0 -> 0.5429484027723726
 2955/3780 0.400 0.040 scale StandardScaler() 0 -> 0.5337245926377419
 2956/3780 0.400 0.040 scale MinMaxScaler()   0 -> 0.5312797253652944
 2957/3780 0.400 0.040 auto StandardScaler() 0 -> 0.5337245926377419
 2958/3780 0.400 0.040 auto MinMaxScaler()   0 -> 0.6210057570933828
 2959/3780 0.400 0.040 0.01 StandardScaler() 0 -> 0.5736865861794029
 2960/3780 0.400 0.040 0.01 MinMaxScaler()   0 -> 0.8129981691226785
 2961/3780 0.400 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5417121355813762
 2962/3780 0.400 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7382250407591773
 2963/3780 0.400 0.040 0.1 StandardScaler() 0 -> 0.5348181233670221
 2964/3780 0.400 0.040 0.1 MinMaxScaler()   0 -> 0.6121301911010145
 2965/3780 0.400 0.040 0.31622776601683794 StandardScaler() 0 -> 0.6046238079170788
 2966/3780 0.400 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5652613531934959
 2967/3780 0.400 0.040 1.0 StandardScaler() 0 -> 0.8160525325134449
 2968/3780 0.400 0.040 1.0 MinMaxScaler()   0 -> 0.5379203953148218
 2969/3780 0.400 0.051 scale StandardScaler() 0 -> 0.5267008518859969
 2970/3780 0.400 0.051 scale MinMaxScaler()   0 -> 0.5261103875778492
 2971/3780 0.400 0.051 auto StandardScaler() 0 -> 0.5267008518859969
 2972/3780 0.400 0.051 auto MinMaxScaler()   0 -> 0.6014070772452453
 2973/3780 0.400 0.051 0.01 StandardScaler() 0 -> 0.5658640873708746
 2974/3780 0.400 0.051 0.01 MinMaxScaler()   0 -> 0.8025989057553824
 2975/3780 0.400 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5356783626178911
 2976/3780 0.400 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7131506629916439
 2977/3780 0.400 0.051 0.1 StandardScaler() 0 -> 0.5270182646134587
 2978/3780 0.400 0.051 0.1 MinMaxScaler()   0 -> 0.5958439972785499
 2979/3780 0.400 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5879041814149022
 2980/3780 0.400 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.5587385843170211
 2981/3780 0.400 0.051 1.0 StandardScaler() 0 -> 0.806679387596645
 2982/3780 0.400 0.051 1.0 MinMaxScaler()   0 -> 0.5320111101496882
 2983/3780 0.400 0.065 scale StandardScaler() 0 -> 0.5201308430772483
 2984/3780 0.400 0.065 scale MinMaxScaler()   0 -> 0.5197750723134164
 2985/3780 0.400 0.065 auto StandardScaler() 0 -> 0.5201308430772483
 2986/3780 0.400 0.065 auto MinMaxScaler()   0 -> 0.5904792759567128
 2987/3780 0.400 0.065 0.01 StandardScaler() 0 -> 0.5588862042615987
 2988/3780 0.400 0.065 0.01 MinMaxScaler()   0 -> 0.7897805012464697
 2989/3780 0.400 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5293191788017105
 2990/3780 0.400 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.6853646263037497
 2991/3780 0.400 0.065 0.1 StandardScaler() 0 -> 0.5205092834923937
 2992/3780 0.400 0.065 0.1 MinMaxScaler()   0 -> 0.5870119203555703
 2993/3780 0.400 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5715054344079241
 2994/3780 0.400 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.5532442596306376
 2995/3780 0.400 0.065 1.0 StandardScaler() 0 -> 0.7950032257894232
 2996/3780 0.400 0.065 1.0 MinMaxScaler()   0 -> 0.5268381108506868
 2997/3780 0.400 0.082 scale StandardScaler() 0 -> 0.5143632133464473
 2998/3780 0.400 0.082 scale MinMaxScaler()   0 -> 0.5144522371037206
 2999/3780 0.400 0.082 auto StandardScaler() 0 -> 0.5143632133464472
 3000/3780 0.400 0.082 auto MinMaxScaler()   0 -> 0.5827873598788526
 3001/3780 0.400 0.082 0.01 StandardScaler() 0 -> 0.5534950426863142
 3002/3780 0.400 0.082 0.01 MinMaxScaler()   0 -> 0.7741150979626927
 3003/3780 0.400 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5238548223801088
 3004/3780 0.400 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6559115558463979
 3005/3780 0.400 0.082 0.1 StandardScaler() 0 -> 0.514588028012363
 3006/3780 0.400 0.082 0.1 MinMaxScaler()   0 -> 0.5790556183928253
 3007/3780 0.400 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5563963119513129
 3008/3780 0.400 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5485562918660166
 3009/3780 0.400 0.082 1.0 StandardScaler() 0 -> 0.7801926249247196
 3010/3780 0.400 0.082 1.0 MinMaxScaler()   0 -> 0.5217177043457567
 3011/3780 0.400 0.104 scale StandardScaler() 0 -> 0.5087236720093002
 3012/3780 0.400 0.104 scale MinMaxScaler()   0 -> 0.5094314995733907
 3013/3780 0.400 0.104 auto StandardScaler() 0 -> 0.5087236720093004
 3014/3780 0.400 0.104 auto MinMaxScaler()   0 -> 0.5749438497179288
 3015/3780 0.400 0.104 0.01 StandardScaler() 0 -> 0.5480294400604001
 3016/3780 0.400 0.104 0.01 MinMaxScaler()   0 -> 0.7551977854304687
 3017/3780 0.400 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5189572053455077
 3018/3780 0.400 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6281849793143305
 3019/3780 0.400 0.104 0.1 StandardScaler() 0 -> 0.5091649806692891
 3020/3780 0.400 0.104 0.1 MinMaxScaler()   0 -> 0.5724850444752624
 3021/3780 0.400 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5436726051110093
 3022/3780 0.400 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5435740252088351
 3023/3780 0.400 0.104 1.0 StandardScaler() 0 -> 0.7607929276369673
 3024/3780 0.400 0.104 1.0 MinMaxScaler()   0 -> 0.5174738980407328
 3025/3780 0.400 0.132 scale StandardScaler() 0 -> 0.5030918122317974
 3026/3780 0.400 0.132 scale MinMaxScaler()   0 -> 0.504746910690706
 3027/3780 0.400 0.132 auto StandardScaler() 0 -> 0.5030918122317973
 3028/3780 0.400 0.132 auto MinMaxScaler()   0 -> 0.5692377377679888
 3029/3780 0.400 0.132 0.01 StandardScaler() 0 -> 0.5431444351273748
 3030/3780 0.400 0.132 0.01 MinMaxScaler()   0 -> 0.7327306467748471
 3031/3780 0.400 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5138320836161911
 3032/3780 0.400 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6061711207143669
 3033/3780 0.400 0.132 0.1 StandardScaler() 0 -> 0.5031543408767091
 3034/3780 0.400 0.132 0.1 MinMaxScaler()   0 -> 0.5668118774384848
 3035/3780 0.400 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5324128304749546
 3036/3780 0.400 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5396108846877797
 3037/3780 0.400 0.132 1.0 StandardScaler() 0 -> 0.7350113201811442
 3038/3780 0.400 0.132 1.0 MinMaxScaler()   0 -> 0.5136585545912687
 3039/3780 0.400 0.168 scale StandardScaler() 0 -> 0.49827301716284184
 3040/3780 0.400 0.168 scale MinMaxScaler()   0 -> 0.5001014162847675
 3041/3780 0.400 0.168 auto StandardScaler() 0 -> 0.49827301716284195
 3042/3780 0.400 0.168 auto MinMaxScaler()   0 -> 0.5640141189147504
 3043/3780 0.400 0.168 0.01 StandardScaler() 0 -> 0.5388036529286124
 3044/3780 0.400 0.168 0.01 MinMaxScaler()   0 -> 0.7069708368378683
 3045/3780 0.400 0.168 0.03162277660168379 StandardScaler() 0 -> 0.509737997357936
 3046/3780 0.400 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.5940867670710918
 3047/3780 0.400 0.168 0.1 StandardScaler() 0 -> 0.49820292346390876
 3048/3780 0.400 0.168 0.1 MinMaxScaler()   0 -> 0.5620003398532087
 3049/3780 0.400 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5220524923936584
 3050/3780 0.400 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5354172512991435
 3051/3780 0.400 0.168 1.0 StandardScaler() 0 -> 0.7016941625003478
 3052/3780 0.400 0.168 1.0 MinMaxScaler()   0 -> 0.5093628801199975
 3053/3780 0.400 0.213 scale StandardScaler() 0 -> 0.494011221254413
 3054/3780 0.400 0.213 scale MinMaxScaler()   0 -> 0.49596722097347407
 3055/3780 0.400 0.213 auto StandardScaler() 0 -> 0.49401122125441294
 3056/3780 0.400 0.213 auto MinMaxScaler()   0 -> 0.5596741604621274
 3057/3780 0.400 0.213 0.01 StandardScaler() 0 -> 0.5336959537838143
 3058/3780 0.400 0.213 0.01 MinMaxScaler()   0 -> 0.6790552508479121
 3059/3780 0.400 0.213 0.03162277660168379 StandardScaler() 0 -> 0.5061384999265526
 3060/3780 0.400 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5861406709003388
 3061/3780 0.400 0.213 0.1 StandardScaler() 0 -> 0.49390802767560044
 3062/3780 0.400 0.213 0.1 MinMaxScaler()   0 -> 0.5576382898341596
 3063/3780 0.400 0.213 0.31622776601683794 StandardScaler() 0 -> 0.512948095070345
 3064/3780 0.400 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5305728114242041
 3065/3780 0.400 0.213 1.0 StandardScaler() 0 -> 0.6631525338109469
 3066/3780 0.400 0.213 1.0 MinMaxScaler()   0 -> 0.5059255147386342
 3067/3780 0.400 0.270 scale StandardScaler() 0 -> 0.4909650652217312
 3068/3780 0.400 0.270 scale MinMaxScaler()   0 -> 0.49209745002280575
 3069/3780 0.400 0.270 auto StandardScaler() 0 -> 0.4909650652217313
 3070/3780 0.400 0.270 auto MinMaxScaler()   0 -> 0.5559041860113079
 3071/3780 0.400 0.270 0.01 StandardScaler() 0 -> 0.5291803458185429
 3072/3780 0.400 0.270 0.01 MinMaxScaler()   0 -> 0.6496845430736249
 3073/3780 0.400 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5014958429527155
 3074/3780 0.400 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5784651273783412
 3075/3780 0.400 0.270 0.1 StandardScaler() 0 -> 0.4910418833951178
 3076/3780 0.400 0.270 0.1 MinMaxScaler()   0 -> 0.5541989594218824
 3077/3780 0.400 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5044009120838527
 3078/3780 0.400 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5265533731574995
 3079/3780 0.400 0.270 1.0 StandardScaler() 0 -> 0.625563948544136
 3080/3780 0.400 0.270 1.0 MinMaxScaler()   0 -> 0.5024887974727145
 3081/3780 0.400 0.342 scale StandardScaler() 0 -> 0.48799612521050584
 3082/3780 0.400 0.342 scale MinMaxScaler()   0 -> 0.4890141940387001
 3083/3780 0.400 0.342 auto StandardScaler() 0 -> 0.4879961252105059
 3084/3780 0.400 0.342 auto MinMaxScaler()   0 -> 0.552415524104167
 3085/3780 0.400 0.342 0.01 StandardScaler() 0 -> 0.5244925891736525
 3086/3780 0.400 0.342 0.01 MinMaxScaler()   0 -> 0.6234823601948883
 3087/3780 0.400 0.342 0.03162277660168379 StandardScaler() 0 -> 0.4979935966838442
 3088/3780 0.400 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5724501323455945
 3089/3780 0.400 0.342 0.1 StandardScaler() 0 -> 0.4876844762980357
 3090/3780 0.400 0.342 0.1 MinMaxScaler()   0 -> 0.5502782027447526
 3091/3780 0.400 0.342 0.31622776601683794 StandardScaler() 0 -> 0.4964469995755947
 3092/3780 0.400 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5222537034828229
 3093/3780 0.400 0.342 1.0 StandardScaler() 0 -> 0.591776641783705
 3094/3780 0.400 0.342 1.0 MinMaxScaler()   0 -> 0.49909311644188703
 3095/3780 0.400 0.434 scale StandardScaler() 0 -> 0.48523668652971647
 3096/3780 0.400 0.434 scale MinMaxScaler()   0 -> 0.48600649031451787
 3097/3780 0.400 0.434 auto StandardScaler() 0 -> 0.48523668652971635
 3098/3780 0.400 0.434 auto MinMaxScaler()   0 -> 0.5487230772286869
 3099/3780 0.400 0.434 0.01 StandardScaler() 0 -> 0.5208572494399182
 3100/3780 0.400 0.434 0.01 MinMaxScaler()   0 -> 0.6034109754435261
 3101/3780 0.400 0.434 0.03162277660168379 StandardScaler() 0 -> 0.49554286504916645
 3102/3780 0.400 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.5678561195289135
 3103/3780 0.400 0.434 0.1 StandardScaler() 0 -> 0.48508805576954633
 3104/3780 0.400 0.434 0.1 MinMaxScaler()   0 -> 0.5470427892591695
 3105/3780 0.400 0.434 0.31622776601683794 StandardScaler() 0 -> 0.48891334601908376
 3106/3780 0.400 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5185413657592646
 3107/3780 0.400 0.434 1.0 StandardScaler() 0 -> 0.5646041786473788
 3108/3780 0.400 0.434 1.0 MinMaxScaler()   0 -> 0.4958384823655755
 3109/3780 0.400 0.551 scale StandardScaler() 0 -> 0.4830624008774733
 3110/3780 0.400 0.551 scale MinMaxScaler()   0 -> 0.4835618695177663
 3111/3780 0.400 0.551 auto StandardScaler() 0 -> 0.4830624008774735
 3112/3780 0.400 0.551 auto MinMaxScaler()   0 -> 0.5456661746676906
 3113/3780 0.400 0.551 0.01 StandardScaler() 0 -> 0.5170698393074645
 3114/3780 0.400 0.551 0.01 MinMaxScaler()   0 -> 0.5925300770879938
 3115/3780 0.400 0.551 0.03162277660168379 StandardScaler() 0 -> 0.49328042499747865
 3116/3780 0.400 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5638738330727576
 3117/3780 0.400 0.551 0.1 StandardScaler() 0 -> 0.48256536229966834
 3118/3780 0.400 0.551 0.1 MinMaxScaler()   0 -> 0.5434627229855735
 3119/3780 0.400 0.551 0.31622776601683794 StandardScaler() 0 -> 0.4823673782628632
 3120/3780 0.400 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.5148652430419317
 3121/3780 0.400 0.551 1.0 StandardScaler() 0 -> 0.5446603234054801
 3122/3780 0.400 0.551 1.0 MinMaxScaler()   0 -> 0.49340524209488174
 3123/3780 0.400 0.700 scale StandardScaler() 0 -> 0.48066908988849244
 3124/3780 0.400 0.700 scale MinMaxScaler()   0 -> 0.48094134034246405
 3125/3780 0.400 0.700 auto StandardScaler() 0 -> 0.4806690898884924
 3126/3780 0.400 0.700 auto MinMaxScaler()   0 -> 0.5426613958151362
 3127/3780 0.400 0.700 0.01 StandardScaler() 0 -> 0.5133019939056495
 3128/3780 0.400 0.700 0.01 MinMaxScaler()   0 -> 0.5848631371379541
 3129/3780 0.400 0.700 0.03162277660168379 StandardScaler() 0 -> 0.4910117146249175
 3130/3780 0.400 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5607820571881922
 3131/3780 0.400 0.700 0.1 StandardScaler() 0 -> 0.4795266581642923
 3132/3780 0.400 0.700 0.1 MinMaxScaler()   0 -> 0.5401649665714888
 3133/3780 0.400 0.700 0.31622776601683794 StandardScaler() 0 -> 0.476997215231028
 3134/3780 0.400 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5117972208282345
 3135/3780 0.400 0.700 1.0 StandardScaler() 0 -> 0.5328793679798695
 3136/3780 0.400 0.700 1.0 MinMaxScaler()   0 -> 0.49138176192703725
 3137/3780 0.400 0.888 scale StandardScaler() 0 -> 0.4777500029180041
 3138/3780 0.400 0.888 scale MinMaxScaler()   0 -> 0.47891550899731766
 3139/3780 0.400 0.888 auto StandardScaler() 0 -> 0.477750002918004
 3140/3780 0.400 0.888 auto MinMaxScaler()   0 -> 0.5391298931534565
 3141/3780 0.400 0.888 0.01 StandardScaler() 0 -> 0.5088570435405795
 3142/3780 0.400 0.888 0.01 MinMaxScaler()   0 -> 0.5777641756915767
 3143/3780 0.400 0.888 0.03162277660168379 StandardScaler() 0 -> 0.48881157773083106
 3144/3780 0.400 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5576754815231237
 3145/3780 0.400 0.888 0.1 StandardScaler() 0 -> 0.47623279159703547
 3146/3780 0.400 0.888 0.1 MinMaxScaler()   0 -> 0.5370763936464894
 3147/3780 0.400 0.888 0.31622776601683794 StandardScaler() 0 -> 0.4734836065085777
 3148/3780 0.400 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5081035309280103
 3149/3780 0.400 0.888 1.0 StandardScaler() 0 -> 0.526401180850836
 3150/3780 0.400 0.888 1.0 MinMaxScaler()   0 -> 0.4896454299639155
 3151/3780 0.400 1.126 scale StandardScaler() 0 -> 0.4750256062436031
 3152/3780 0.400 1.126 scale MinMaxScaler()   0 -> 0.4769259402736279
 3153/3780 0.400 1.126 auto StandardScaler() 0 -> 0.47502560624360335
 3154/3780 0.400 1.126 auto MinMaxScaler()   0 -> 0.5364853010582277
 3155/3780 0.400 1.126 0.01 StandardScaler() 0 -> 0.504792939451162
 3156/3780 0.400 1.126 0.01 MinMaxScaler()   0 -> 0.5727617352478814
 3157/3780 0.400 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48743221846641555
 3158/3780 0.400 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5551747853930892
 3159/3780 0.400 1.126 0.1 StandardScaler() 0 -> 0.4735593619582416
 3160/3780 0.400 1.126 0.1 MinMaxScaler()   0 -> 0.5343643393699312
 3161/3780 0.400 1.126 0.31622776601683794 StandardScaler() 0 -> 0.4708473306480491
 3162/3780 0.400 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5051890996048714
 3163/3780 0.400 1.126 1.0 StandardScaler() 0 -> 0.5208868770717522
 3164/3780 0.400 1.126 1.0 MinMaxScaler()   0 -> 0.4873786448634214
 3165/3780 0.400 1.429 scale StandardScaler() 0 -> 0.4727170174426658
 3166/3780 0.400 1.429 scale MinMaxScaler()   0 -> 0.47465091318931446
 3167/3780 0.400 1.429 auto StandardScaler() 0 -> 0.472717017442666
 3168/3780 0.400 1.429 auto MinMaxScaler()   0 -> 0.5335971816772237
 3169/3780 0.400 1.429 0.01 StandardScaler() 0 -> 0.5015595522667914
 3170/3780 0.400 1.429 0.01 MinMaxScaler()   0 -> 0.5679119620440285
 3171/3780 0.400 1.429 0.03162277660168379 StandardScaler() 0 -> 0.48628534781754634
 3172/3780 0.400 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5525473322326201
 3173/3780 0.400 1.429 0.1 StandardScaler() 0 -> 0.47134048510466026
 3174/3780 0.400 1.429 0.1 MinMaxScaler()   0 -> 0.5312805613186778
 3175/3780 0.400 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4699784506517804
 3176/3780 0.400 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5021707428781097
 3177/3780 0.400 1.429 1.0 StandardScaler() 0 -> 0.5167481299758928
 3178/3780 0.400 1.429 1.0 MinMaxScaler()   0 -> 0.48499337783802016
 3179/3780 0.400 1.814 scale StandardScaler() 0 -> 0.47114229500157023
 3180/3780 0.400 1.814 scale MinMaxScaler()   0 -> 0.47344113805972704
 3181/3780 0.400 1.814 auto StandardScaler() 0 -> 0.4711422950015707
 3182/3780 0.400 1.814 auto MinMaxScaler()   0 -> 0.5304991643644775
 3183/3780 0.400 1.814 0.01 StandardScaler() 0 -> 0.49929815544719375
 3184/3780 0.400 1.814 0.01 MinMaxScaler()   0 -> 0.5645232965017151
 3185/3780 0.400 1.814 0.03162277660168379 StandardScaler() 0 -> 0.48519767031643973
 3186/3780 0.400 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5504968906707647
 3187/3780 0.400 1.814 0.1 StandardScaler() 0 -> 0.47057611983256864
 3188/3780 0.400 1.814 0.1 MinMaxScaler()   0 -> 0.527782256840791
 3189/3780 0.400 1.814 0.31622776601683794 StandardScaler() 0 -> 0.4708177278902239
 3190/3780 0.400 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.4999036757182253
 3191/3780 0.400 1.814 1.0 StandardScaler() 0 -> 0.5154294420703834
 3192/3780 0.400 1.814 1.0 MinMaxScaler()   0 -> 0.48306303306623427
 3193/3780 0.400 2.302 scale StandardScaler() 0 -> 0.47067669080014657
 3194/3780 0.400 2.302 scale MinMaxScaler()   0 -> 0.4730641052547237
 3195/3780 0.400 2.302 auto StandardScaler() 0 -> 0.4706766908001469
 3196/3780 0.400 2.302 auto MinMaxScaler()   0 -> 0.5270209943383825
 3197/3780 0.400 2.302 0.01 StandardScaler() 0 -> 0.49760536365312574
 3198/3780 0.400 2.302 0.01 MinMaxScaler()   0 -> 0.5617626048744455
 3199/3780 0.400 2.302 0.03162277660168379 StandardScaler() 0 -> 0.48409890353664337
 3200/3780 0.400 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5485655550673436
 3201/3780 0.400 2.302 0.1 StandardScaler() 0 -> 0.4713030154051163
 3202/3780 0.400 2.302 0.1 MinMaxScaler()   0 -> 0.5240658688521589
 3203/3780 0.400 2.302 0.31622776601683794 StandardScaler() 0 -> 0.4730724253201926
 3204/3780 0.400 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.4976167484673047
 3205/3780 0.400 2.302 1.0 StandardScaler() 0 -> 0.5151621695866774
 3206/3780 0.400 2.302 1.0 MinMaxScaler()   0 -> 0.4816072017901763
 3207/3780 0.400 2.921 scale StandardScaler() 0 -> 0.4718523757934272
 3208/3780 0.400 2.921 scale MinMaxScaler()   0 -> 0.47330124249210037
 3209/3780 0.400 2.921 auto StandardScaler() 0 -> 0.4718523757934268
 3210/3780 0.400 2.921 auto MinMaxScaler()   0 -> 0.5233190804084765
 3211/3780 0.400 2.921 0.01 StandardScaler() 0 -> 0.4959693574760841
 3212/3780 0.400 2.921 0.01 MinMaxScaler()   0 -> 0.5590073626756619
 3213/3780 0.400 2.921 0.03162277660168379 StandardScaler() 0 -> 0.48277571107254674
 3214/3780 0.400 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.546572436061472
 3215/3780 0.400 2.921 0.1 StandardScaler() 0 -> 0.47299551430776016
 3216/3780 0.400 2.921 0.1 MinMaxScaler()   0 -> 0.5203253552880543
 3217/3780 0.400 2.921 0.31622776601683794 StandardScaler() 0 -> 0.477467068708976
 3218/3780 0.400 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.49593086302178463
 3219/3780 0.400 2.921 1.0 StandardScaler() 0 -> 0.5151065676055252
 3220/3780 0.400 2.921 1.0 MinMaxScaler()   0 -> 0.480445576411405
 3221/3780 0.400 3.707 scale StandardScaler() 0 -> 0.4738984813233416
 3222/3780 0.400 3.707 scale MinMaxScaler()   0 -> 0.47364939036508225
 3223/3780 0.400 3.707 auto StandardScaler() 0 -> 0.47389848132334217
 3224/3780 0.400 3.707 auto MinMaxScaler()   0 -> 0.5194400476154013
 3225/3780 0.400 3.707 0.01 StandardScaler() 0 -> 0.49485444179874527
 3226/3780 0.400 3.707 0.01 MinMaxScaler()   0 -> 0.5572919795733057
 3227/3780 0.400 3.707 0.03162277660168379 StandardScaler() 0 -> 0.4812563289464398
 3228/3780 0.400 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5444481355958161
 3229/3780 0.400 3.707 0.1 StandardScaler() 0 -> 0.4746977752082347
 3230/3780 0.400 3.707 0.1 MinMaxScaler()   0 -> 0.5168859710027296
 3231/3780 0.400 3.707 0.31622776601683794 StandardScaler() 0 -> 0.4814879923096138
 3232/3780 0.400 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.4940189531058226
 3233/3780 0.400 3.707 1.0 StandardScaler() 0 -> 0.515103518306116
 3234/3780 0.400 3.707 1.0 MinMaxScaler()   0 -> 0.47933322263714323
 3235/3780 0.400 4.703 scale StandardScaler() 0 -> 0.4761154189608927
 3236/3780 0.400 4.703 scale MinMaxScaler()   0 -> 0.4750310333734074
 3237/3780 0.400 4.703 auto StandardScaler() 0 -> 0.4761154189608931
 3238/3780 0.400 4.703 auto MinMaxScaler()   0 -> 0.5163059635443469
 3239/3780 0.400 4.703 0.01 StandardScaler() 0 -> 0.49391744100038487
 3240/3780 0.400 4.703 0.01 MinMaxScaler()   0 -> 0.5547219075934394
 3241/3780 0.400 4.703 0.03162277660168379 StandardScaler() 0 -> 0.4807566222370924
 3242/3780 0.400 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.542473496664772
 3243/3780 0.400 4.703 0.1 StandardScaler() 0 -> 0.47620759852881184
 3244/3780 0.400 4.703 0.1 MinMaxScaler()   0 -> 0.5142186010987535
 3245/3780 0.400 4.703 0.31622776601683794 StandardScaler() 0 -> 0.4853568083650794
 3246/3780 0.400 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.49299596451072597
 3247/3780 0.400 4.703 1.0 StandardScaler() 0 -> 0.5151068521918094
 3248/3780 0.400 4.703 1.0 MinMaxScaler()   0 -> 0.4784549560535409
 3249/3780 0.400 5.968 scale StandardScaler() 0 -> 0.4782243877523376
 3250/3780 0.400 5.968 scale MinMaxScaler()   0 -> 0.4777302747584533
 3251/3780 0.400 5.968 auto StandardScaler() 0 -> 0.47822438775233783
 3252/3780 0.400 5.968 auto MinMaxScaler()   0 -> 0.5134866158230502
 3253/3780 0.400 5.968 0.01 StandardScaler() 0 -> 0.4930595159424322
 3254/3780 0.400 5.968 0.01 MinMaxScaler()   0 -> 0.5536150638768133
 3255/3780 0.400 5.968 0.03162277660168379 StandardScaler() 0 -> 0.48015509747598034
 3256/3780 0.400 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5399559459451543
 3257/3780 0.400 5.968 0.1 StandardScaler() 0 -> 0.479303107185786
 3258/3780 0.400 5.968 0.1 MinMaxScaler()   0 -> 0.511090144649015
 3259/3780 0.400 5.968 0.31622776601683794 StandardScaler() 0 -> 0.48917152374828027
 3260/3780 0.400 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.49247159323970413
 3261/3780 0.400 5.968 1.0 StandardScaler() 0 -> 0.5151068521918094
 3262/3780 0.400 5.968 1.0 MinMaxScaler()   0 -> 0.4775319182999092
 3263/3780 0.400 7.574 scale StandardScaler() 0 -> 0.4816257920448706
 3264/3780 0.400 7.574 scale MinMaxScaler()   0 -> 0.48034063096029983
 3265/3780 0.400 7.574 auto StandardScaler() 0 -> 0.4816257920448687
 3266/3780 0.400 7.574 auto MinMaxScaler()   0 -> 0.5104478906806118
 3267/3780 0.400 7.574 0.01 StandardScaler() 0 -> 0.4926150296849913
 3268/3780 0.400 7.574 0.01 MinMaxScaler()   0 -> 0.5524464610406308
 3269/3780 0.400 7.574 0.03162277660168379 StandardScaler() 0 -> 0.4786628413851955
 3270/3780 0.400 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5376422089990255
 3271/3780 0.400 7.574 0.1 StandardScaler() 0 -> 0.4847402336433295
 3272/3780 0.400 7.574 0.1 MinMaxScaler()   0 -> 0.5080261665219655
 3273/3780 0.400 7.574 0.31622776601683794 StandardScaler() 0 -> 0.4927368473858988
 3274/3780 0.400 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.49203693927946074
 3275/3780 0.400 7.574 1.0 StandardScaler() 0 -> 0.5151068521918094
 3276/3780 0.400 7.574 1.0 MinMaxScaler()   0 -> 0.4770418475118407
 3277/3780 0.400 9.611 scale StandardScaler() 0 -> 0.48747189980500333
 3278/3780 0.400 9.611 scale MinMaxScaler()   0 -> 0.48319162494994905
 3279/3780 0.400 9.611 auto StandardScaler() 0 -> 0.4874718998050029
 3280/3780 0.400 9.611 auto MinMaxScaler()   0 -> 0.5073666103058
 3281/3780 0.400 9.611 0.01 StandardScaler() 0 -> 0.49203371879771834
 3282/3780 0.400 9.611 0.01 MinMaxScaler()   0 -> 0.5514071386381211
 3283/3780 0.400 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4774115241741765
 3284/3780 0.400 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.534578438183844
 3285/3780 0.400 9.611 0.1 StandardScaler() 0 -> 0.49197525844160234
 3286/3780 0.400 9.611 0.1 MinMaxScaler()   0 -> 0.505739088558152
 3287/3780 0.400 9.611 0.31622776601683794 StandardScaler() 0 -> 0.4957515031470971
 3288/3780 0.400 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4914232435349734
 3289/3780 0.400 9.611 1.0 StandardScaler() 0 -> 0.5151068521918094
 3290/3780 0.400 9.611 1.0 MinMaxScaler()   0 -> 0.4766432285345111
 3291/3780 0.400 12.196 scale StandardScaler() 0 -> 0.4953015368858514
 3292/3780 0.400 12.196 scale MinMaxScaler()   0 -> 0.486757086754464
 3293/3780 0.400 12.196 auto StandardScaler() 0 -> 0.49530153688585005
 3294/3780 0.400 12.196 auto MinMaxScaler()   0 -> 0.505280099534264
 3295/3780 0.400 12.196 0.01 StandardScaler() 0 -> 0.49100274196447424
 3296/3780 0.400 12.196 0.01 MinMaxScaler()   0 -> 0.5503686653452013
 3297/3780 0.400 12.196 0.03162277660168379 StandardScaler() 0 -> 0.47713641234035215
 3298/3780 0.400 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5317212418775755
 3299/3780 0.400 12.196 0.1 StandardScaler() 0 -> 0.4997107045590185
 3300/3780 0.400 12.196 0.1 MinMaxScaler()   0 -> 0.5029011864148639
 3301/3780 0.400 12.196 0.31622776601683794 StandardScaler() 0 -> 0.4980952191881396
 3302/3780 0.400 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4903170404798756
 3303/3780 0.400 12.196 1.0 StandardScaler() 0 -> 0.5151068521918094
 3304/3780 0.400 12.196 1.0 MinMaxScaler()   0 -> 0.47665118086217845
 3305/3780 0.400 15.476 scale StandardScaler() 0 -> 0.5035239857054766
 3306/3780 0.400 15.476 scale MinMaxScaler()   0 -> 0.492139421263992
 3307/3780 0.400 15.476 auto StandardScaler() 0 -> 0.5035239857054786
 3308/3780 0.400 15.476 auto MinMaxScaler()   0 -> 0.502665815301469
 3309/3780 0.400 15.476 0.01 StandardScaler() 0 -> 0.4901081206743601
 3310/3780 0.400 15.476 0.01 MinMaxScaler()   0 -> 0.5490832069056902
 3311/3780 0.400 15.476 0.03162277660168379 StandardScaler() 0 -> 0.4778203626125767
 3312/3780 0.400 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5286021570359379
 3313/3780 0.400 15.476 0.1 StandardScaler() 0 -> 0.5096684997173074
 3314/3780 0.400 15.476 0.1 MinMaxScaler()   0 -> 0.5008317371101537
 3315/3780 0.400 15.476 0.31622776601683794 StandardScaler() 0 -> 0.4997921835785659
 3316/3780 0.400 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.48933054196397946
 3317/3780 0.400 15.476 1.0 StandardScaler() 0 -> 0.5151068521918094
 3318/3780 0.400 15.476 1.0 MinMaxScaler()   0 -> 0.4769864139703361
 3319/3780 0.400 19.638 scale StandardScaler() 0 -> 0.5141398304167493
 3320/3780 0.400 19.638 scale MinMaxScaler()   0 -> 0.49941186614632244
 3321/3780 0.400 19.638 auto StandardScaler() 0 -> 0.5141398304167429
 3322/3780 0.400 19.638 auto MinMaxScaler()   0 -> 0.5004840855818836
 3323/3780 0.400 19.638 0.01 StandardScaler() 0 -> 0.4894327924717479
 3324/3780 0.400 19.638 0.01 MinMaxScaler()   0 -> 0.548014852244683
 3325/3780 0.400 19.638 0.03162277660168379 StandardScaler() 0 -> 0.47993071490681044
 3326/3780 0.400 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.5251658136096595
 3327/3780 0.400 19.638 0.1 StandardScaler() 0 -> 0.5203953018743205
 3328/3780 0.400 19.638 0.1 MinMaxScaler()   0 -> 0.4989742577654197
 3329/3780 0.400 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5006359962711633
 3330/3780 0.400 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.48829797358513144
 3331/3780 0.400 19.638 1.0 StandardScaler() 0 -> 0.5151068521918094
 3332/3780 0.400 19.638 1.0 MinMaxScaler()   0 -> 0.4778385861408012
 3333/3780 0.400 24.920 scale StandardScaler() 0 -> 0.5261855630142608
 3334/3780 0.400 24.920 scale MinMaxScaler()   0 -> 0.5078150708770713
 3335/3780 0.400 24.920 auto StandardScaler() 0 -> 0.526185563014258
 3336/3780 0.400 24.920 auto MinMaxScaler()   0 -> 0.4985869761615431
 3337/3780 0.400 24.920 0.01 StandardScaler() 0 -> 0.4884444245665301
 3338/3780 0.400 24.920 0.01 MinMaxScaler()   0 -> 0.5463666605885923
 3339/3780 0.400 24.920 0.03162277660168379 StandardScaler() 0 -> 0.4814162862311819
 3340/3780 0.400 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.5214304049686534
 3341/3780 0.400 24.920 0.1 StandardScaler() 0 -> 0.5331926290487382
 3342/3780 0.400 24.920 0.1 MinMaxScaler()   0 -> 0.4975675657763821
 3343/3780 0.400 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5007979010379894
 3344/3780 0.400 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.48683884016810325
 3345/3780 0.400 24.920 1.0 StandardScaler() 0 -> 0.5151068521918094
 3346/3780 0.400 24.920 1.0 MinMaxScaler()   0 -> 0.48039781720317887
 3347/3780 0.400 31.623 scale StandardScaler() 0 -> 0.5397856882696815
 3348/3780 0.400 31.623 scale MinMaxScaler()   0 -> 0.5170873242012813
 3349/3780 0.400 31.623 auto StandardScaler() 0 -> 0.5397856882696815
 3350/3780 0.400 31.623 auto MinMaxScaler()   0 -> 0.49751022821955804
 3351/3780 0.400 31.623 0.01 StandardScaler() 0 -> 0.4874769721945309
 3352/3780 0.400 31.623 0.01 MinMaxScaler()   0 -> 0.5449252561656763
 3353/3780 0.400 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4826508605766669
 3354/3780 0.400 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.5180616707141478
 3355/3780 0.400 31.623 0.1 StandardScaler() 0 -> 0.5498254044839045
 3356/3780 0.400 31.623 0.1 MinMaxScaler()   0 -> 0.4966962162448815
 3357/3780 0.400 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5009070817431202
 3358/3780 0.400 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.48561689389132345
 3359/3780 0.400 31.623 1.0 StandardScaler() 0 -> 0.5151068521918094
 3360/3780 0.400 31.623 1.0 MinMaxScaler()   0 -> 0.48317723286465325
 3361/3780 0.450 0.032 scale StandardScaler() 0 -> 0.548329305544668
 3362/3780 0.450 0.032 scale MinMaxScaler()   0 -> 0.5441761813001467
 3363/3780 0.450 0.032 auto StandardScaler() 0 -> 0.548329305544668
 3364/3780 0.450 0.032 auto MinMaxScaler()   0 -> 0.6608142610575046
 3365/3780 0.450 0.032 0.01 StandardScaler() 0 -> 0.5833469667070273
 3366/3780 0.450 0.032 0.01 MinMaxScaler()   0 -> 0.8531470041813182
 3367/3780 0.450 0.032 0.03162277660168379 StandardScaler() 0 -> 0.5538068394748552
 3368/3780 0.450 0.032 0.03162277660168379 MinMaxScaler()   0 -> 0.787812139731233
 3369/3780 0.450 0.032 0.1 StandardScaler() 0 -> 0.5498688643977955
 3370/3780 0.450 0.032 0.1 MinMaxScaler()   0 -> 0.6476538775985855
 3371/3780 0.450 0.032 0.31622776601683794 StandardScaler() 0 -> 0.6340989202051883
 3372/3780 0.450 0.032 0.31622776601683794 MinMaxScaler()   0 -> 0.5757372196488215
 3373/3780 0.450 0.032 1.0 StandardScaler() 0 -> 0.8576703363665431
 3374/3780 0.450 0.032 1.0 MinMaxScaler()   0 -> 0.5499360385711161
 3375/3780 0.450 0.040 scale StandardScaler() 0 -> 0.5398092718810226
 3376/3780 0.450 0.040 scale MinMaxScaler()   0 -> 0.5375526572571406
 3377/3780 0.450 0.040 auto StandardScaler() 0 -> 0.5398092718810226
 3378/3780 0.450 0.040 auto MinMaxScaler()   0 -> 0.6279942512500145
 3379/3780 0.450 0.040 0.01 StandardScaler() 0 -> 0.5758274321945419
 3380/3780 0.450 0.040 0.01 MinMaxScaler()   0 -> 0.8441990007274072
 3381/3780 0.450 0.040 0.03162277660168379 StandardScaler() 0 -> 0.5472833602387016
 3382/3780 0.450 0.040 0.03162277660168379 MinMaxScaler()   0 -> 0.7648817502442892
 3383/3780 0.450 0.040 0.1 StandardScaler() 0 -> 0.5410184165150601
 3384/3780 0.450 0.040 0.1 MinMaxScaler()   0 -> 0.617635274685311
 3385/3780 0.450 0.040 0.31622776601683794 StandardScaler() 0 -> 0.6146173865367905
 3386/3780 0.450 0.040 0.31622776601683794 MinMaxScaler()   0 -> 0.5692419281138137
 3387/3780 0.450 0.040 1.0 StandardScaler() 0 -> 0.849640327270361
 3388/3780 0.450 0.040 1.0 MinMaxScaler()   0 -> 0.5432400331724413
 3389/3780 0.450 0.051 scale StandardScaler() 0 -> 0.5329687078325699
 3390/3780 0.450 0.051 scale MinMaxScaler()   0 -> 0.5313441211180433
 3391/3780 0.450 0.051 auto StandardScaler() 0 -> 0.5329687078325699
 3392/3780 0.450 0.051 auto MinMaxScaler()   0 -> 0.6045601008283735
 3393/3780 0.450 0.051 0.01 StandardScaler() 0 -> 0.5688344369109554
 3394/3780 0.450 0.051 0.01 MinMaxScaler()   0 -> 0.8330745072573075
 3395/3780 0.450 0.051 0.03162277660168379 StandardScaler() 0 -> 0.5406726947894772
 3396/3780 0.450 0.051 0.03162277660168379 MinMaxScaler()   0 -> 0.7376978221637858
 3397/3780 0.450 0.051 0.1 StandardScaler() 0 -> 0.5339141803639528
 3398/3780 0.450 0.051 0.1 MinMaxScaler()   0 -> 0.5988178956918199
 3399/3780 0.450 0.051 0.31622776601683794 StandardScaler() 0 -> 0.5965860752820512
 3400/3780 0.450 0.051 0.31622776601683794 MinMaxScaler()   0 -> 0.56354355294863
 3401/3780 0.450 0.051 1.0 StandardScaler() 0 -> 0.8388722593103832
 3402/3780 0.450 0.051 1.0 MinMaxScaler()   0 -> 0.5377894077088874
 3403/3780 0.450 0.065 scale StandardScaler() 0 -> 0.5264135312431435
 3404/3780 0.450 0.065 scale MinMaxScaler()   0 -> 0.5258433236248675
 3405/3780 0.450 0.065 auto StandardScaler() 0 -> 0.5264135312431435
 3406/3780 0.450 0.065 auto MinMaxScaler()   0 -> 0.5919609610293778
 3407/3780 0.450 0.065 0.01 StandardScaler() 0 -> 0.5631805895179575
 3408/3780 0.450 0.065 0.01 MinMaxScaler()   0 -> 0.8193624789290509
 3409/3780 0.450 0.065 0.03162277660168379 StandardScaler() 0 -> 0.5343863814421671
 3410/3780 0.450 0.065 0.03162277660168379 MinMaxScaler()   0 -> 0.7056749779623521
 3411/3780 0.450 0.065 0.1 StandardScaler() 0 -> 0.5269058964861433
 3412/3780 0.450 0.065 0.1 MinMaxScaler()   0 -> 0.5880252826344311
 3413/3780 0.450 0.065 0.31622776601683794 StandardScaler() 0 -> 0.5795590634363732
 3414/3780 0.450 0.065 0.31622776601683794 MinMaxScaler()   0 -> 0.55787566088346
 3415/3780 0.450 0.065 1.0 StandardScaler() 0 -> 0.8239669312842199
 3416/3780 0.450 0.065 1.0 MinMaxScaler()   0 -> 0.5320826533174238
 3417/3780 0.450 0.082 scale StandardScaler() 0 -> 0.5197884838609933
 3418/3780 0.450 0.082 scale MinMaxScaler()   0 -> 0.5202082452372366
 3419/3780 0.450 0.082 auto StandardScaler() 0 -> 0.5197884838609933
 3420/3780 0.450 0.082 auto MinMaxScaler()   0 -> 0.583719587164917
 3421/3780 0.450 0.082 0.01 StandardScaler() 0 -> 0.5574473789703127
 3422/3780 0.450 0.082 0.01 MinMaxScaler()   0 -> 0.8026721628508512
 3423/3780 0.450 0.082 0.03162277660168379 StandardScaler() 0 -> 0.5279530146683359
 3424/3780 0.450 0.082 0.03162277660168379 MinMaxScaler()   0 -> 0.6703232497178601
 3425/3780 0.450 0.082 0.1 StandardScaler() 0 -> 0.5199653253740318
 3426/3780 0.450 0.082 0.1 MinMaxScaler()   0 -> 0.5806785231098631
 3427/3780 0.450 0.082 0.31622776601683794 StandardScaler() 0 -> 0.5642666755390963
 3428/3780 0.450 0.082 0.31622776601683794 MinMaxScaler()   0 -> 0.5527634674258924
 3429/3780 0.450 0.082 1.0 StandardScaler() 0 -> 0.8031700338752698
 3430/3780 0.450 0.082 1.0 MinMaxScaler()   0 -> 0.5277055438440268
 3431/3780 0.450 0.104 scale StandardScaler() 0 -> 0.5131164339707927
 3432/3780 0.450 0.104 scale MinMaxScaler()   0 -> 0.5148889196876282
 3433/3780 0.450 0.104 auto StandardScaler() 0 -> 0.5131164339707927
 3434/3780 0.450 0.104 auto MinMaxScaler()   0 -> 0.5769514723685526
 3435/3780 0.450 0.104 0.01 StandardScaler() 0 -> 0.5524490787058486
 3436/3780 0.450 0.104 0.01 MinMaxScaler()   0 -> 0.783137261381722
 3437/3780 0.450 0.104 0.03162277660168379 StandardScaler() 0 -> 0.5232612257434305
 3438/3780 0.450 0.104 0.03162277660168379 MinMaxScaler()   0 -> 0.6364814480343091
 3439/3780 0.450 0.104 0.1 StandardScaler() 0 -> 0.5132483482638236
 3440/3780 0.450 0.104 0.1 MinMaxScaler()   0 -> 0.5746977986409688
 3441/3780 0.450 0.104 0.31622776601683794 StandardScaler() 0 -> 0.5502920675419452
 3442/3780 0.450 0.104 0.31622776601683794 MinMaxScaler()   0 -> 0.5475403503544571
 3443/3780 0.450 0.104 1.0 StandardScaler() 0 -> 0.7745648327931062
 3444/3780 0.450 0.104 1.0 MinMaxScaler()   0 -> 0.5225841547334992
 3445/3780 0.450 0.132 scale StandardScaler() 0 -> 0.5070682101188038
 3446/3780 0.450 0.132 scale MinMaxScaler()   0 -> 0.5105138653517433
 3447/3780 0.450 0.132 auto StandardScaler() 0 -> 0.5070682101188038
 3448/3780 0.450 0.132 auto MinMaxScaler()   0 -> 0.5712521501593449
 3449/3780 0.450 0.132 0.01 StandardScaler() 0 -> 0.5466859040457744
 3450/3780 0.450 0.132 0.01 MinMaxScaler()   0 -> 0.7589119137924603
 3451/3780 0.450 0.132 0.03162277660168379 StandardScaler() 0 -> 0.5187489662221116
 3452/3780 0.450 0.132 0.03162277660168379 MinMaxScaler()   0 -> 0.6106711745146388
 3453/3780 0.450 0.132 0.1 StandardScaler() 0 -> 0.5069145277697659
 3454/3780 0.450 0.132 0.1 MinMaxScaler()   0 -> 0.5692625648410284
 3455/3780 0.450 0.132 0.31622776601683794 StandardScaler() 0 -> 0.5387185100249176
 3456/3780 0.450 0.132 0.31622776601683794 MinMaxScaler()   0 -> 0.5421176352063579
 3457/3780 0.450 0.132 1.0 StandardScaler() 0 -> 0.738297997691975
 3458/3780 0.450 0.132 1.0 MinMaxScaler()   0 -> 0.5187613482036829
 3459/3780 0.450 0.168 scale StandardScaler() 0 -> 0.5023922769043548
 3460/3780 0.450 0.168 scale MinMaxScaler()   0 -> 0.5060952602673088
 3461/3780 0.450 0.168 auto StandardScaler() 0 -> 0.5023922769043547
 3462/3780 0.450 0.168 auto MinMaxScaler()   0 -> 0.5666283374617075
 3463/3780 0.450 0.168 0.01 StandardScaler() 0 -> 0.5410051593324656
 3464/3780 0.450 0.168 0.01 MinMaxScaler()   0 -> 0.7313698512822485
 3465/3780 0.450 0.168 0.03162277660168379 StandardScaler() 0 -> 0.5147500188755025
 3466/3780 0.450 0.168 0.03162277660168379 MinMaxScaler()   0 -> 0.5960930286960427
 3467/3780 0.450 0.168 0.1 StandardScaler() 0 -> 0.502405500777195
 3468/3780 0.450 0.168 0.1 MinMaxScaler()   0 -> 0.5646519521427399
 3469/3780 0.450 0.168 0.31622776601683794 StandardScaler() 0 -> 0.5278479497302363
 3470/3780 0.450 0.168 0.31622776601683794 MinMaxScaler()   0 -> 0.5380979759812704
 3471/3780 0.450 0.168 1.0 StandardScaler() 0 -> 0.6964813578513406
 3472/3780 0.450 0.168 1.0 MinMaxScaler()   0 -> 0.5149137209118145
 3473/3780 0.450 0.213 scale StandardScaler() 0 -> 0.4984654204984908
 3474/3780 0.450 0.213 scale MinMaxScaler()   0 -> 0.5011330942845506
 3475/3780 0.450 0.213 auto StandardScaler() 0 -> 0.4984654204984907
 3476/3780 0.450 0.213 auto MinMaxScaler()   0 -> 0.5620683146583151
 3477/3780 0.450 0.213 0.01 StandardScaler() 0 -> 0.5359508468346318
 3478/3780 0.450 0.213 0.01 MinMaxScaler()   0 -> 0.6974258496238607
 3479/3780 0.450 0.213 0.03162277660168379 StandardScaler() 0 -> 0.510417833896471
 3480/3780 0.450 0.213 0.03162277660168379 MinMaxScaler()   0 -> 0.5864750962421338
 3481/3780 0.450 0.213 0.1 StandardScaler() 0 -> 0.4986182611393351
 3482/3780 0.450 0.213 0.1 MinMaxScaler()   0 -> 0.5602932820151909
 3483/3780 0.450 0.213 0.31622776601683794 StandardScaler() 0 -> 0.5186297973426893
 3484/3780 0.450 0.213 0.31622776601683794 MinMaxScaler()   0 -> 0.5338610406289681
 3485/3780 0.450 0.213 1.0 StandardScaler() 0 -> 0.6547963093819892
 3486/3780 0.450 0.213 1.0 MinMaxScaler()   0 -> 0.5101148471239428
 3487/3780 0.450 0.270 scale StandardScaler() 0 -> 0.4954001247393011
 3488/3780 0.450 0.270 scale MinMaxScaler()   0 -> 0.49729722970554224
 3489/3780 0.450 0.270 auto StandardScaler() 0 -> 0.495400124739301
 3490/3780 0.450 0.270 auto MinMaxScaler()   0 -> 0.5582427098153947
 3491/3780 0.450 0.270 0.01 StandardScaler() 0 -> 0.5316199399576913
 3492/3780 0.450 0.270 0.01 MinMaxScaler()   0 -> 0.6623886455895489
 3493/3780 0.450 0.270 0.03162277660168379 StandardScaler() 0 -> 0.5061558327074694
 3494/3780 0.450 0.270 0.03162277660168379 MinMaxScaler()   0 -> 0.5798994748920396
 3495/3780 0.450 0.270 0.1 StandardScaler() 0 -> 0.4957087982019835
 3496/3780 0.450 0.270 0.1 MinMaxScaler()   0 -> 0.5563291779061618
 3497/3780 0.450 0.270 0.31622776601683794 StandardScaler() 0 -> 0.5095591077117908
 3498/3780 0.450 0.270 0.31622776601683794 MinMaxScaler()   0 -> 0.5297921909197886
 3499/3780 0.450 0.270 1.0 StandardScaler() 0 -> 0.6184905149992
 3500/3780 0.450 0.270 1.0 MinMaxScaler()   0 -> 0.5070817839649965
 3501/3780 0.450 0.342 scale StandardScaler() 0 -> 0.49235457177274844
 3502/3780 0.450 0.342 scale MinMaxScaler()   0 -> 0.4942663282552579
 3503/3780 0.450 0.342 auto StandardScaler() 0 -> 0.49235457177274844
 3504/3780 0.450 0.342 auto MinMaxScaler()   0 -> 0.5544768679083251
 3505/3780 0.450 0.342 0.01 StandardScaler() 0 -> 0.5275895942986853
 3506/3780 0.450 0.342 0.01 MinMaxScaler()   0 -> 0.6297901728947867
 3507/3780 0.450 0.342 0.03162277660168379 StandardScaler() 0 -> 0.5025762942957317
 3508/3780 0.450 0.342 0.03162277660168379 MinMaxScaler()   0 -> 0.5746572399306884
 3509/3780 0.450 0.342 0.1 StandardScaler() 0 -> 0.49214893456141295
 3510/3780 0.450 0.342 0.1 MinMaxScaler()   0 -> 0.552314367298062
 3511/3780 0.450 0.342 0.31622776601683794 StandardScaler() 0 -> 0.5012043413821323
 3512/3780 0.450 0.342 0.31622776601683794 MinMaxScaler()   0 -> 0.5261721051811181
 3513/3780 0.450 0.342 1.0 StandardScaler() 0 -> 0.588377323000724
 3514/3780 0.450 0.342 1.0 MinMaxScaler()   0 -> 0.5036566367933278
 3515/3780 0.450 0.434 scale StandardScaler() 0 -> 0.48938831433947794
 3516/3780 0.450 0.434 scale MinMaxScaler()   0 -> 0.49182199677413246
 3517/3780 0.450 0.434 auto StandardScaler() 0 -> 0.489388314339478
 3518/3780 0.450 0.434 auto MinMaxScaler()   0 -> 0.5507968085350808
 3519/3780 0.450 0.434 0.01 StandardScaler() 0 -> 0.5237165065600841
 3520/3780 0.450 0.434 0.01 MinMaxScaler()   0 -> 0.6064480145060581
 3521/3780 0.450 0.434 0.03162277660168379 StandardScaler() 0 -> 0.4997598342415159
 3522/3780 0.450 0.434 0.03162277660168379 MinMaxScaler()   0 -> 0.569807319501548
 3523/3780 0.450 0.434 0.1 StandardScaler() 0 -> 0.4891840776632437
 3524/3780 0.450 0.434 0.1 MinMaxScaler()   0 -> 0.5488338064411834
 3525/3780 0.450 0.434 0.31622776601683794 StandardScaler() 0 -> 0.4943842813213431
 3526/3780 0.450 0.434 0.31622776601683794 MinMaxScaler()   0 -> 0.5219702803265261
 3527/3780 0.450 0.434 1.0 StandardScaler() 0 -> 0.5657233582584346
 3528/3780 0.450 0.434 1.0 MinMaxScaler()   0 -> 0.500590631984735
 3529/3780 0.450 0.551 scale StandardScaler() 0 -> 0.48677763847071165
 3530/3780 0.450 0.551 scale MinMaxScaler()   0 -> 0.4889219018845577
 3531/3780 0.450 0.551 auto StandardScaler() 0 -> 0.4867776384707116
 3532/3780 0.450 0.551 auto MinMaxScaler()   0 -> 0.5475029495997489
 3533/3780 0.450 0.551 0.01 StandardScaler() 0 -> 0.5198053582427539
 3534/3780 0.450 0.551 0.01 MinMaxScaler()   0 -> 0.5937545591822401
 3535/3780 0.450 0.551 0.03162277660168379 StandardScaler() 0 -> 0.4967171913616813
 3536/3780 0.450 0.551 0.03162277660168379 MinMaxScaler()   0 -> 0.5657560559183609
 3537/3780 0.450 0.551 0.1 StandardScaler() 0 -> 0.48600666042463897
 3538/3780 0.450 0.551 0.1 MinMaxScaler()   0 -> 0.5457653857928663
 3539/3780 0.450 0.551 0.31622776601683794 StandardScaler() 0 -> 0.48812586529926416
 3540/3780 0.450 0.551 0.31622776601683794 MinMaxScaler()   0 -> 0.51807955993581
 3541/3780 0.450 0.551 1.0 StandardScaler() 0 -> 0.5496182637926837
 3542/3780 0.450 0.551 1.0 MinMaxScaler()   0 -> 0.4973882098701765
 3543/3780 0.450 0.700 scale StandardScaler() 0 -> 0.4836645062745886
 3544/3780 0.450 0.700 scale MinMaxScaler()   0 -> 0.48644100445267857
 3545/3780 0.450 0.700 auto StandardScaler() 0 -> 0.48366450627458857
 3546/3780 0.450 0.700 auto MinMaxScaler()   0 -> 0.5445999119968875
 3547/3780 0.450 0.700 0.01 StandardScaler() 0 -> 0.51512025264153
 3548/3780 0.450 0.700 0.01 MinMaxScaler()   0 -> 0.5852953258421691
 3549/3780 0.450 0.700 0.03162277660168379 StandardScaler() 0 -> 0.49423063114326077
 3550/3780 0.450 0.700 0.03162277660168379 MinMaxScaler()   0 -> 0.5622921576385879
 3551/3780 0.450 0.700 0.1 StandardScaler() 0 -> 0.4825290420100758
 3552/3780 0.450 0.700 0.1 MinMaxScaler()   0 -> 0.542601694969468
 3553/3780 0.450 0.700 0.31622776601683794 StandardScaler() 0 -> 0.4830543602243327
 3554/3780 0.450 0.700 0.31622776601683794 MinMaxScaler()   0 -> 0.5144427927010632
 3555/3780 0.450 0.700 1.0 StandardScaler() 0 -> 0.5405128811351695
 3556/3780 0.450 0.700 1.0 MinMaxScaler()   0 -> 0.4943550279636759
 3557/3780 0.450 0.888 scale StandardScaler() 0 -> 0.4811168378108977
 3558/3780 0.450 0.888 scale MinMaxScaler()   0 -> 0.48353840811072324
 3559/3780 0.450 0.888 auto StandardScaler() 0 -> 0.4811168378108976
 3560/3780 0.450 0.888 auto MinMaxScaler()   0 -> 0.5416048560436558
 3561/3780 0.450 0.888 0.01 StandardScaler() 0 -> 0.5116871213236055
 3562/3780 0.450 0.888 0.01 MinMaxScaler()   0 -> 0.5788423644053456
 3563/3780 0.450 0.888 0.03162277660168379 StandardScaler() 0 -> 0.4921078739072911
 3564/3780 0.450 0.888 0.03162277660168379 MinMaxScaler()   0 -> 0.5594025422027321
 3565/3780 0.450 0.888 0.1 StandardScaler() 0 -> 0.4798651313968756
 3566/3780 0.450 0.888 0.1 MinMaxScaler()   0 -> 0.5395714838620568
 3567/3780 0.450 0.888 0.31622776601683794 StandardScaler() 0 -> 0.47921380767238514
 3568/3780 0.450 0.888 0.31622776601683794 MinMaxScaler()   0 -> 0.5113005756350928
 3569/3780 0.450 0.888 1.0 StandardScaler() 0 -> 0.534293241823662
 3570/3780 0.450 0.888 1.0 MinMaxScaler()   0 -> 0.49173141425621764
 3571/3780 0.450 1.126 scale StandardScaler() 0 -> 0.47881740475166046
 3572/3780 0.450 1.126 scale MinMaxScaler()   0 -> 0.48095593830641
 3573/3780 0.450 1.126 auto StandardScaler() 0 -> 0.4788174047516606
 3574/3780 0.450 1.126 auto MinMaxScaler()   0 -> 0.538777246331529
 3575/3780 0.450 1.126 0.01 StandardScaler() 0 -> 0.508347023103202
 3576/3780 0.450 1.126 0.01 MinMaxScaler()   0 -> 0.5741007110037734
 3577/3780 0.450 1.126 0.03162277660168379 StandardScaler() 0 -> 0.48977264361357636
 3578/3780 0.450 1.126 0.03162277660168379 MinMaxScaler()   0 -> 0.5567529238970904
 3579/3780 0.450 1.126 0.1 StandardScaler() 0 -> 0.4774491552063668
 3580/3780 0.450 1.126 0.1 MinMaxScaler()   0 -> 0.5364371501233297
 3581/3780 0.450 1.126 0.31622776601683794 StandardScaler() 0 -> 0.4766216522691495
 3582/3780 0.450 1.126 0.31622776601683794 MinMaxScaler()   0 -> 0.5085940608129274
 3583/3780 0.450 1.126 1.0 StandardScaler() 0 -> 0.5285495572989034
 3584/3780 0.450 1.126 1.0 MinMaxScaler()   0 -> 0.489678932396644
 3585/3780 0.450 1.429 scale StandardScaler() 0 -> 0.47686351691198275
 3586/3780 0.450 1.429 scale MinMaxScaler()   0 -> 0.47910766516359615
 3587/3780 0.450 1.429 auto StandardScaler() 0 -> 0.47686351691198325
 3588/3780 0.450 1.429 auto MinMaxScaler()   0 -> 0.5354004570689422
 3589/3780 0.450 1.429 0.01 StandardScaler() 0 -> 0.5058377813507223
 3590/3780 0.450 1.429 0.01 MinMaxScaler()   0 -> 0.5699713537205832
 3591/3780 0.450 1.429 0.03162277660168379 StandardScaler() 0 -> 0.4884472527341371
 3592/3780 0.450 1.429 0.03162277660168379 MinMaxScaler()   0 -> 0.5541553721021799
 3593/3780 0.450 1.429 0.1 StandardScaler() 0 -> 0.4755583305641609
 3594/3780 0.450 1.429 0.1 MinMaxScaler()   0 -> 0.5326438481085489
 3595/3780 0.450 1.429 0.31622776601683794 StandardScaler() 0 -> 0.4758708328189518
 3596/3780 0.450 1.429 0.31622776601683794 MinMaxScaler()   0 -> 0.5059396314077337
 3597/3780 0.450 1.429 1.0 StandardScaler() 0 -> 0.5243544643226684
 3598/3780 0.450 1.429 1.0 MinMaxScaler()   0 -> 0.48834402911282576
 3599/3780 0.450 1.814 scale StandardScaler() 0 -> 0.4754635217066419
 3600/3780 0.450 1.814 scale MinMaxScaler()   0 -> 0.47802238601797503
 3601/3780 0.450 1.814 auto StandardScaler() 0 -> 0.475463521706642
 3602/3780 0.450 1.814 auto MinMaxScaler()   0 -> 0.5315286541450747
 3603/3780 0.450 1.814 0.01 StandardScaler() 0 -> 0.5030795699909756
 3604/3780 0.450 1.814 0.01 MinMaxScaler()   0 -> 0.5656030898993977
 3605/3780 0.450 1.814 0.03162277660168379 StandardScaler() 0 -> 0.48780764092691414
 3606/3780 0.450 1.814 0.03162277660168379 MinMaxScaler()   0 -> 0.5519704178249606
 3607/3780 0.450 1.814 0.1 StandardScaler() 0 -> 0.4747651270560531
 3608/3780 0.450 1.814 0.1 MinMaxScaler()   0 -> 0.5290761378631353
 3609/3780 0.450 1.814 0.31622776601683794 StandardScaler() 0 -> 0.47660453115566676
 3610/3780 0.450 1.814 0.31622776601683794 MinMaxScaler()   0 -> 0.503689767297458
 3611/3780 0.450 1.814 1.0 StandardScaler() 0 -> 0.5233349793033802
 3612/3780 0.450 1.814 1.0 MinMaxScaler()   0 -> 0.4878061206527929
 3613/3780 0.450 2.302 scale StandardScaler() 0 -> 0.4749901112961426
 3614/3780 0.450 2.302 scale MinMaxScaler()   0 -> 0.477070864930063
 3615/3780 0.450 2.302 auto StandardScaler() 0 -> 0.4749901112961424
 3616/3780 0.450 2.302 auto MinMaxScaler()   0 -> 0.5281635982483964
 3617/3780 0.450 2.302 0.01 StandardScaler() 0 -> 0.5004752946529579
 3618/3780 0.450 2.302 0.01 MinMaxScaler()   0 -> 0.5630554097372046
 3619/3780 0.450 2.302 0.03162277660168379 StandardScaler() 0 -> 0.4866795530926287
 3620/3780 0.450 2.302 0.03162277660168379 MinMaxScaler()   0 -> 0.5497943232361223
 3621/3780 0.450 2.302 0.1 StandardScaler() 0 -> 0.47494446676718455
 3622/3780 0.450 2.302 0.1 MinMaxScaler()   0 -> 0.525972953501438
 3623/3780 0.450 2.302 0.31622776601683794 StandardScaler() 0 -> 0.47840633381637093
 3624/3780 0.450 2.302 0.31622776601683794 MinMaxScaler()   0 -> 0.501502929437826
 3625/3780 0.450 2.302 1.0 StandardScaler() 0 -> 0.523085684413927
 3626/3780 0.450 2.302 1.0 MinMaxScaler()   0 -> 0.4866920892637309
 3627/3780 0.450 2.921 scale StandardScaler() 0 -> 0.4754912718497278
 3628/3780 0.450 2.921 scale MinMaxScaler()   0 -> 0.4768390566075529
 3629/3780 0.450 2.921 auto StandardScaler() 0 -> 0.47549127184972767
 3630/3780 0.450 2.921 auto MinMaxScaler()   0 -> 0.5252916102719324
 3631/3780 0.450 2.921 0.01 StandardScaler() 0 -> 0.49877506849453185
 3632/3780 0.450 2.921 0.01 MinMaxScaler()   0 -> 0.5602289769405954
 3633/3780 0.450 2.921 0.03162277660168379 StandardScaler() 0 -> 0.4849953861427175
 3634/3780 0.450 2.921 0.03162277660168379 MinMaxScaler()   0 -> 0.5481557172429515
 3635/3780 0.450 2.921 0.1 StandardScaler() 0 -> 0.4761239378167235
 3636/3780 0.450 2.921 0.1 MinMaxScaler()   0 -> 0.5231185727064414
 3637/3780 0.450 2.921 0.31622776601683794 StandardScaler() 0 -> 0.4823937857784313
 3638/3780 0.450 2.921 0.31622776601683794 MinMaxScaler()   0 -> 0.4989338081267052
 3639/3780 0.450 2.921 1.0 StandardScaler() 0 -> 0.5230664091874556
 3640/3780 0.450 2.921 1.0 MinMaxScaler()   0 -> 0.4855672265030469
 3641/3780 0.450 3.707 scale StandardScaler() 0 -> 0.4772278902614377
 3642/3780 0.450 3.707 scale MinMaxScaler()   0 -> 0.4767284216612319
 3643/3780 0.450 3.707 auto StandardScaler() 0 -> 0.4772278902614376
 3644/3780 0.450 3.707 auto MinMaxScaler()   0 -> 0.522240831334729
 3645/3780 0.450 3.707 0.01 StandardScaler() 0 -> 0.49712307034900216
 3646/3780 0.450 3.707 0.01 MinMaxScaler()   0 -> 0.5583866188665437
 3647/3780 0.450 3.707 0.03162277660168379 StandardScaler() 0 -> 0.4838249328611503
 3648/3780 0.450 3.707 0.03162277660168379 MinMaxScaler()   0 -> 0.5463972993740644
 3649/3780 0.450 3.707 0.1 StandardScaler() 0 -> 0.477735364318494
 3650/3780 0.450 3.707 0.1 MinMaxScaler()   0 -> 0.5197111911131597
 3651/3780 0.450 3.707 0.31622776601683794 StandardScaler() 0 -> 0.4858428965277037
 3652/3780 0.450 3.707 0.31622776601683794 MinMaxScaler()   0 -> 0.49670500146784885
 3653/3780 0.450 3.707 1.0 StandardScaler() 0 -> 0.5230672085652285
 3654/3780 0.450 3.707 1.0 MinMaxScaler()   0 -> 0.4834964969867565
 3655/3780 0.450 4.703 scale StandardScaler() 0 -> 0.47928282870511757
 3656/3780 0.450 4.703 scale MinMaxScaler()   0 -> 0.47800374093473375
 3657/3780 0.450 4.703 auto StandardScaler() 0 -> 0.47928282870511785
 3658/3780 0.450 4.703 auto MinMaxScaler()   0 -> 0.5189821727806853
 3659/3780 0.450 4.703 0.01 StandardScaler() 0 -> 0.49566829921756694
 3660/3780 0.450 4.703 0.01 MinMaxScaler()   0 -> 0.5562954795268881
 3661/3780 0.450 4.703 0.03162277660168379 StandardScaler() 0 -> 0.48255485326466524
 3662/3780 0.450 4.703 0.03162277660168379 MinMaxScaler()   0 -> 0.5442281135492104
 3663/3780 0.450 4.703 0.1 StandardScaler() 0 -> 0.4799050262970251
 3664/3780 0.450 4.703 0.1 MinMaxScaler()   0 -> 0.5165557298770461
 3665/3780 0.450 4.703 0.31622776601683794 StandardScaler() 0 -> 0.4893874938974667
 3666/3780 0.450 4.703 0.31622776601683794 MinMaxScaler()   0 -> 0.4954014964848139
 3667/3780 0.450 4.703 1.0 StandardScaler() 0 -> 0.5230672085652285
 3668/3780 0.450 4.703 1.0 MinMaxScaler()   0 -> 0.4819775691022819
 3669/3780 0.450 5.968 scale StandardScaler() 0 -> 0.4819286758433099
 3670/3780 0.450 5.968 scale MinMaxScaler()   0 -> 0.48041631122935824
 3671/3780 0.450 5.968 auto StandardScaler() 0 -> 0.48192867584331106
 3672/3780 0.450 5.968 auto MinMaxScaler()   0 -> 0.516043557006964
 3673/3780 0.450 5.968 0.01 StandardScaler() 0 -> 0.4947095094489355
 3674/3780 0.450 5.968 0.01 MinMaxScaler()   0 -> 0.5552218802256977
 3675/3780 0.450 5.968 0.03162277660168379 StandardScaler() 0 -> 0.4815068049020044
 3676/3780 0.450 5.968 0.03162277660168379 MinMaxScaler()   0 -> 0.5414611209803764
 3677/3780 0.450 5.968 0.1 StandardScaler() 0 -> 0.4838634428791044
 3678/3780 0.450 5.968 0.1 MinMaxScaler()   0 -> 0.5137150684909848
 3679/3780 0.450 5.968 0.31622776601683794 StandardScaler() 0 -> 0.49282297447630746
 3680/3780 0.450 5.968 0.31622776601683794 MinMaxScaler()   0 -> 0.49474655944401086
 3681/3780 0.450 5.968 1.0 StandardScaler() 0 -> 0.5230672085652285
 3682/3780 0.450 5.968 1.0 MinMaxScaler()   0 -> 0.48178490678741515
 3683/3780 0.450 7.574 scale StandardScaler() 0 -> 0.48622730321944374
 3684/3780 0.450 7.574 scale MinMaxScaler()   0 -> 0.4832762129743844
 3685/3780 0.450 7.574 auto StandardScaler() 0 -> 0.48622730321944324
 3686/3780 0.450 7.574 auto MinMaxScaler()   0 -> 0.5130486020787126
 3687/3780 0.450 7.574 0.01 StandardScaler() 0 -> 0.49422168058201893
 3688/3780 0.450 7.574 0.01 MinMaxScaler()   0 -> 0.5542321059379841
 3689/3780 0.450 7.574 0.03162277660168379 StandardScaler() 0 -> 0.4808972796228373
 3690/3780 0.450 7.574 0.03162277660168379 MinMaxScaler()   0 -> 0.5385062603475402
 3691/3780 0.450 7.574 0.1 StandardScaler() 0 -> 0.48931086632585763
 3692/3780 0.450 7.574 0.1 MinMaxScaler()   0 -> 0.5109541404753029
 3693/3780 0.450 7.574 0.31622776601683794 StandardScaler() 0 -> 0.49598755121601035
 3694/3780 0.450 7.574 0.31622776601683794 MinMaxScaler()   0 -> 0.49375537388711893
 3695/3780 0.450 7.574 1.0 StandardScaler() 0 -> 0.5230672085652285
 3696/3780 0.450 7.574 1.0 MinMaxScaler()   0 -> 0.48165136735374475
 3697/3780 0.450 9.611 scale StandardScaler() 0 -> 0.4919668443725418
 3698/3780 0.450 9.611 scale MinMaxScaler()   0 -> 0.4859598690559693
 3699/3780 0.450 9.611 auto StandardScaler() 0 -> 0.4919668443725427
 3700/3780 0.450 9.611 auto MinMaxScaler()   0 -> 0.5103578860854224
 3701/3780 0.450 9.611 0.01 StandardScaler() 0 -> 0.49324445726478544
 3702/3780 0.450 9.611 0.01 MinMaxScaler()   0 -> 0.5529042147267652
 3703/3780 0.450 9.611 0.03162277660168379 StandardScaler() 0 -> 0.4804365978343666
 3704/3780 0.450 9.611 0.03162277660168379 MinMaxScaler()   0 -> 0.5356913530352142
 3705/3780 0.450 9.611 0.1 StandardScaler() 0 -> 0.4943396587633375
 3706/3780 0.450 9.611 0.1 MinMaxScaler()   0 -> 0.5080585335624469
 3707/3780 0.450 9.611 0.31622776601683794 StandardScaler() 0 -> 0.4984768781670465
 3708/3780 0.450 9.611 0.31622776601683794 MinMaxScaler()   0 -> 0.4926644223183408
 3709/3780 0.450 9.611 1.0 StandardScaler() 0 -> 0.5230672085652285
 3710/3780 0.450 9.611 1.0 MinMaxScaler()   0 -> 0.4810729887991023
 3711/3780 0.450 12.196 scale StandardScaler() 0 -> 0.49762422356224084
 3712/3780 0.450 12.196 scale MinMaxScaler()   0 -> 0.48965948547372773
 3713/3780 0.450 12.196 auto StandardScaler() 0 -> 0.4976242235622399
 3714/3780 0.450 12.196 auto MinMaxScaler()   0 -> 0.5076589264890908
 3715/3780 0.450 12.196 0.01 StandardScaler() 0 -> 0.49240785758419564
 3716/3780 0.450 12.196 0.01 MinMaxScaler()   0 -> 0.5518542337668982
 3717/3780 0.450 12.196 0.03162277660168379 StandardScaler() 0 -> 0.48116686568195854
 3718/3780 0.450 12.196 0.03162277660168379 MinMaxScaler()   0 -> 0.5324168267396852
 3719/3780 0.450 12.196 0.1 StandardScaler() 0 -> 0.5009846687980516
 3720/3780 0.450 12.196 0.1 MinMaxScaler()   0 -> 0.5059413402181124
 3721/3780 0.450 12.196 0.31622776601683794 StandardScaler() 0 -> 0.5004765447703856
 3722/3780 0.450 12.196 0.31622776601683794 MinMaxScaler()   0 -> 0.4919141657665061
 3723/3780 0.450 12.196 1.0 StandardScaler() 0 -> 0.5230672085652285
 3724/3780 0.450 12.196 1.0 MinMaxScaler()   0 -> 0.48038364901538183
 3725/3780 0.450 15.476 scale StandardScaler() 0 -> 0.5047365592634129
 3726/3780 0.450 15.476 scale MinMaxScaler()   0 -> 0.49506587337324554
 3727/3780 0.450 15.476 auto StandardScaler() 0 -> 0.5047365592634164
 3728/3780 0.450 15.476 auto MinMaxScaler()   0 -> 0.5053561152431744
 3729/3780 0.450 15.476 0.01 StandardScaler() 0 -> 0.4918578169314339
 3730/3780 0.450 15.476 0.01 MinMaxScaler()   0 -> 0.5505728563180313
 3731/3780 0.450 15.476 0.03162277660168379 StandardScaler() 0 -> 0.48146276770874064
 3732/3780 0.450 15.476 0.03162277660168379 MinMaxScaler()   0 -> 0.5291962464389601
 3733/3780 0.450 15.476 0.1 StandardScaler() 0 -> 0.5093493948057078
 3734/3780 0.450 15.476 0.1 MinMaxScaler()   0 -> 0.5034277362583172
 3735/3780 0.450 15.476 0.31622776601683794 StandardScaler() 0 -> 0.5017976134804054
 3736/3780 0.450 15.476 0.31622776601683794 MinMaxScaler()   0 -> 0.4912866793959098
 3737/3780 0.450 15.476 1.0 StandardScaler() 0 -> 0.5230672085652285
 3738/3780 0.450 15.476 1.0 MinMaxScaler()   0 -> 0.4801692991466444
 3739/3780 0.450 19.638 scale StandardScaler() 0 -> 0.5141452040445386
 3740/3780 0.450 19.638 scale MinMaxScaler()   0 -> 0.5024293464693843
 3741/3780 0.450 19.638 auto StandardScaler() 0 -> 0.5141452040445376
 3742/3780 0.450 19.638 auto MinMaxScaler()   0 -> 0.503079097024306
 3743/3780 0.450 19.638 0.01 StandardScaler() 0 -> 0.49110172384394674
 3744/3780 0.450 19.638 0.01 MinMaxScaler()   0 -> 0.5494348714522407
 3745/3780 0.450 19.638 0.03162277660168379 StandardScaler() 0 -> 0.4821482289352581
 3746/3780 0.450 19.638 0.03162277660168379 MinMaxScaler()   0 -> 0.526520149850148
 3747/3780 0.450 19.638 0.1 StandardScaler() 0 -> 0.5196198599296006
 3748/3780 0.450 19.638 0.1 MinMaxScaler()   0 -> 0.5019154139809947
 3749/3780 0.450 19.638 0.31622776601683794 StandardScaler() 0 -> 0.5021695996974057
 3750/3780 0.450 19.638 0.31622776601683794 MinMaxScaler()   0 -> 0.4900862856487918
 3751/3780 0.450 19.638 1.0 StandardScaler() 0 -> 0.5230672085652285
 3752/3780 0.450 19.638 1.0 MinMaxScaler()   0 -> 0.4806350858753663
 3753/3780 0.450 24.920 scale StandardScaler() 0 -> 0.5249427149004148
 3754/3780 0.450 24.920 scale MinMaxScaler()   0 -> 0.5102501609425348
 3755/3780 0.450 24.920 auto StandardScaler() 0 -> 0.5249427149004184
 3756/3780 0.450 24.920 auto MinMaxScaler()   0 -> 0.5015272495335558
 3757/3780 0.450 24.920 0.01 StandardScaler() 0 -> 0.4900757537357469
 3758/3780 0.450 24.920 0.01 MinMaxScaler()   0 -> 0.5481295515522341
 3759/3780 0.450 24.920 0.03162277660168379 StandardScaler() 0 -> 0.483083649438017
 3760/3780 0.450 24.920 0.03162277660168379 MinMaxScaler()   0 -> 0.524097329119726
 3761/3780 0.450 24.920 0.1 StandardScaler() 0 -> 0.5331724716092653
 3762/3780 0.450 24.920 0.1 MinMaxScaler()   0 -> 0.4998945610153169
 3763/3780 0.450 24.920 0.31622776601683794 StandardScaler() 0 -> 0.5021776436214379
 3764/3780 0.450 24.920 0.31622776601683794 MinMaxScaler()   0 -> 0.48933255769971734
 3765/3780 0.450 24.920 1.0 StandardScaler() 0 -> 0.5230672085652285
 3766/3780 0.450 24.920 1.0 MinMaxScaler()   0 -> 0.48215646321790934
 3767/3780 0.450 31.623 scale StandardScaler() 0 -> 0.5395830427520681
 3768/3780 0.450 31.623 scale MinMaxScaler()   0 -> 0.5185130421278207
 3769/3780 0.450 31.623 auto StandardScaler() 0 -> 0.5395830427520715
 3770/3780 0.450 31.623 auto MinMaxScaler()   0 -> 0.499747598747004
 3771/3780 0.450 31.623 0.01 StandardScaler() 0 -> 0.4892514712875644
 3772/3780 0.450 31.623 0.01 MinMaxScaler()   0 -> 0.5468627745301934
 3773/3780 0.450 31.623 0.03162277660168379 StandardScaler() 0 -> 0.4849994699217743
 3774/3780 0.450 31.623 0.03162277660168379 MinMaxScaler()   0 -> 0.520928594564566
 3775/3780 0.450 31.623 0.1 StandardScaler() 0 -> 0.5491989079112227
 3776/3780 0.450 31.623 0.1 MinMaxScaler()   0 -> 0.49886775305217396
 3777/3780 0.450 31.623 0.31622776601683794 StandardScaler() 0 -> 0.5021776436214379
 3778/3780 0.450 31.623 0.31622776601683794 MinMaxScaler()   0 -> 0.4883316009019687
 3779/3780 0.450 31.623 1.0 StandardScaler() 0 -> 0.5230672085652285
 3780/3780 0.450 31.623 1.0 MinMaxScaler()   0 -> 0.48348583377622817
Time Taken 10583.299s

Evaluation des Hyperparametertrainings

Im folgenden Kapitel werden die durch Kreuzvalidierung beurteilten Modelle des Random Forest Regressors und der Support Vector Regression untersucht. Das Ziel dieser Evaluation wird sein, die besten Hyperparameter für die jeweiligen Algorithmen zu wählen und für jeden Algorithmus ein adäquates Modell zu trainieren. Diese Modelle werden auf die Testdaten angewandt und sollten damit auch eine gute Generalisierung erreichen.

Random Forest Regressor

Im Nachfolgenden werden die Ergebnisse des Hyperparametertrainings für den Random Forest Regressor dargestellt. Man sieht hier deutlich, dass wir den Besten Mean Squared Error auf den Validierungsdaten bei einer Baumtiefe (max_depth) von 23 und einer minimalen Anzahl an Spaltungen (min_sample_split) von 2 erreichen.

Bei den Funktionen zur Berechnung der maximalen Anzahl an Features, die für eine Aufteilung betrachtet werden, stechen zwei Funktionen heraus. Dies wäre zum Einen die Wurzelfunktion (sqrt), welche von Scikit-Learn durch $round(sqrt(11))$ berechnet wird und zum anderen die Funktion des Logarithmus Dualis, welche durch Scikit-Learn mit $round(log2(11))$ dargestellt wird.

Bei 11 Features ergibt sich, dass die beide Funktionen dasselbe Ergebnis liefern:

$round(log2(11)) = round(sqrt(11))$

Dies ist uns leider erst nach dem Durchlauf der Hyperparameter aufgefallen. Im Laufenden werden wir mit der Wurzelfunktion weiterarbeiten.

df = pd.read_csv('results/train_conf_tree.csv')  
df=df.sort_values(by=['mse_val'])
df.head(5)

	n_estimators	max_depth	min_samples_split	min_weight_fraction_leaf	ccp_alpha	oob_score	max_features	r2score_train	mse_train	acc_train	r2score_val	mse_val	acc_val	time
0	1000	23	2	0	0	True	sqrt	0.932157	0.051272	0.958235	0.490865	0.384372	0.649588	6.287473
1	1000	23	2	0	0	True	log2	0.932157	0.051272	0.958235	0.490865	0.384372	0.649588	6.258244
2	1000	22	2	0	0	True	sqrt	0.931837	0.051515	0.957938	0.490420	0.384711	0.651959	6.288148
3	1000	22	2	0	0	True	log2	0.931837	0.051515	0.957938	0.490420	0.384711	0.651959	6.350337
4	1000	29	2	0	0	True	sqrt	0.932390	0.051096	0.957642	0.490034	0.385001	0.648997	6.302474

Die dreidimensionalen Darstellungen zeigt die Veränderung des Mean Squared Errors mit verändernder maximaler Tiefe (max_depth) und minimaler Aufteilung der Features (min_sample_split).

Zur Ermittlung der optimalen Baumtiefe wird folgend eine zweidimensionale Darstellung verwendet, da hier das Optimum besser erkennbar ist.

plot_rfr3d()

In den beiden unterliegenden Grafiken wird jeweils der Mean Squared Error über die Abhängigkeit der maximale Tiefe und der minimalen Aufteilung der Features dargestellt.

In der ersten Grafik wird dabei die maximale Tiefe der Bäume auf 23 gesetzt und die max_feature Methode sqrt verwendet. Wie bereits oben erläutert, bildet diese Kombination die besten Ergebnisse unser Hyperparametersuche ab. Ab einer Tiefe von 20 wird hier erkennbar, dass sich die Ergebnisse kaum verbessern. Das Optimum liegt hierbei bei einer Baumtiefe von 23. Zudem ist erkennbar, dass sich die Laufzeit bei höherer maximaler Tiefe nicht mehr stark verändert. Dies liegt daran, dass bei einer Tiefe von 23 die maximale Tiefe bereits erreicht ist.

In der zweiten Grafik wird eine minimale Aufteilung der Features von zwei und die max_feature Methode sqrt verwendet. Hier wird deutlich, dass die kleinste Aufteilung von 2 Featuren die besten Ergebnisse Erzielen.

plot_rfr2d_2()

Auswahl des Random Forest Regressors:

Durch die vorherige Evaluation der verschiedenen Modelle ergibt sich, dass der Random Forest Regressor bei einer maximalen Tiefe größer 20 und einer minimalen Aufteilung der Features von 2 die besten Ergebnisse erzielt. Da es ersichtlich ist, dass das Model mit dem geringesten MSE auf die Validationsdaten hier die beste Wahl darstellt, wird für die maximale Tiefe der Wert 23 festgelegt.

Im Folgenden werden die von uns gewählten Hyperparametern mit ihren erzielten Metriken dargestellt:

# Hyperparameter des ausgewählten Modells

df = pd.read_csv('results/train_conf_tree.csv')  
df = df.sort_values(by=['mse_val'])

best_config_tree = df.iloc[0].to_dict()

best_config_tree

{'n_estimators': 1000,
 'max_depth': 23,
 'min_samples_split': 2,
 'min_weight_fraction_leaf': 0,
 'ccp_alpha': 0,
 'oob_score': True,
 'max_features': 'sqrt',
 'r2score_train': 0.9321572452223404,
 'mse_train': 0.0512718609771633,
 'acc_train': 0.95823472695263,
 'r2score_val': 0.4908645412701974,
 'mse_val': 0.3843721213738536,
 'acc_val': 0.6495883165581211,
 'time': 6.287473440170288}

SVR

SVR ohne Feature Selection

Im folgenden Kapitel werden die Ergebnisse der Support Vector Regression ohne Anwendung der Feature Selection evaluiert, um anschließend die Hyperparameter für ein adäquates Modell wählen zu können.

An der Ausgabe der Hyperparameter und deren Validierungs-/Trainingsergebnisse sieht man sehr gut, dass der Durchlauf des Support Verctor Regressors mit der Skalierfunktion StandardScaler die besten Ergebnisse erzielt.

df = pd.read_csv('results/train_conf_svr.csv')  
df=df.sort_values(by=['mse_val'])
df.head(5)

	kernel	epsilon	C	gamma	degree	r2score_train	mse_train	acc_train	r2score_val	mse_val	acc_val	scaler	time
0	rbf	0.25	1.429461	0.31622776601683794	0	0.808053	0.145069	0.898253	0.385928	0.463565	0.605451	StandardScaler()	2.750675
1	rbf	0.30	1.429461	0.31622776601683794	0	0.791919	0.157263	0.901659	0.385446	0.463923	0.607228	StandardScaler()	2.621991
2	rbf	0.25	1.126482	0.31622776601683794	0	0.776587	0.168851	0.870557	0.385359	0.464009	0.605155	StandardScaler()	2.657069
3	rbf	0.20	1.126482	0.31622776601683794	0	0.787608	0.160522	0.865225	0.385237	0.464100	0.599230	StandardScaler()	2.794530
4	rbf	0.20	1.429461	0.31622776601683794	0	0.820345	0.135780	0.892772	0.384590	0.464578	0.606636	StandardScaler()	2.889254

In den beiden unterliegenden Grafiken wird jeweils der Mean Squared Error über die Abhängigkeit der Regularisierungskonstante C und dem Parameter Epsilon dargestellt.

Es ist wie oben erneut erkennbar, dass der StandardScaler bessere Ergebnisse als der MinMaxScaler erreicht. Aus diesem Grund werden hier bereits ausschließlich diese Modelle betrachtet.

In der ersten Grafik ist deutlich zu erkennen, dass die Zeitkomplexität mit größer werdendem Epsilon linear abnimmt.

Die zweite Grafik zeigt, dass der ideale Mean Squared Error bei einer Regularisierungskonstante von etwa 1,429 liegt. Da die Regularisierung im Funktionsaufruf von Scikit-Learn proportional invers zu C ist, bedeutet dies für unser Modell, dass die Toleranz gegenüber größerer Gewichte zu besseren Ergebnissen führt.

plot_svr2d()

In den unteren beiden Grafiken wird der Mean Squared Error abhängig von den beiden Parameter C und Epsilon noch einmal dreidimensional dargestellt.

plot_svr_3d() 

SVR mit Feature Selection

Im Folgenden Kapitel werden die Ergebnisse der Support Vector Regression mit Anwendung der Feature Selection evaluiert um anschließend das beste Modell wählen zu können.

In der Ausgabe der Hyperparameter und deren Validierungs-/Trainingsergebnisse sieht man sehr gut, dass der Durchlauf des Support Vector Regressors mit der Scikit-Learn Funktion MinMaxScaler die besten Ergebnisse erzielt. Aus diesem Grund werden hier bereits ausschließlich Modelle betrachtet, bei der die Daten mit Hilfe von MinMax skaliert wurden.

Für den Parameter Gamma wird der von Scikit-Learn vorgegebene Wert "scale" verwendet.

df = pd.read_csv('results/train_conf_svr_feat_sel.csv')  
df=df.sort_values(by=['mse_val'])
df.head(5)

	kernel	epsilon	C	gamma	degree	r2score_train	mse_train	acc_train	r2score_val	mse_val	acc_val	scaler	time
0	rbf	0.25	0.699564	scale	0	0.357370	0.485698	0.578348	0.293147	0.533736	0.547690	MinMaxScaler()	2.292870
1	rbf	0.25	0.887720	scale	0	0.364004	0.480685	0.582642	0.292724	0.534056	0.546801	MinMaxScaler()	2.296838
2	rbf	0.25	0.551289	scale	0	0.351147	0.490401	0.574349	0.292262	0.534406	0.546506	MinMaxScaler()	2.263947
3	rbf	0.20	0.699564	scale	0	0.356163	0.486611	0.577163	0.292192	0.534473	0.545915	MinMaxScaler()	2.355726
4	rbf	0.25	1.126482	scale	0	0.370623	0.475683	0.587530	0.291829	0.534728	0.547097	MinMaxScaler()	2.344709

In der dreidimensionalen Grafik wird der Mean Suqared Error in Abhängigkeit von den Parametern C und Epsilon grafisch dargestellt. Bereits hier ist zu erkennen, dass unsere Modelle bei Andwendung von Feature Selection eine schlechtere Vorhersage auf die Validierungsdaten vorweisen.

Die Ursache dafür kann am Informationsverlust liegen, wenn Features durch die Feature Selection nicht in den Lernprozess aufgenommen werden.

plot_svr_feat_sel3d()

plot_svr2d_feat_sel()

Auswahl des SVR Modells

Es wird nur ein Modell für die SVR ausgewählt. Da der Support Vector Regressor bessere Ergebnisse auf den nicht selektierten Datensatz erzielt, wird diese Vorgehensweise gewählt. Aus diesem Grund wird nur ein Modell auf den Datensatz ohne Feature Selection trainiert .

# Hyperparameter des ausgewählten Modells

df = pd.read_csv('results/train_conf_svr.csv')  
df = df.sort_values(by=['mse_val'])
best_config_svr = df.iloc[0].to_dict()

best_config_svr

{'kernel': 'rbf',
 'epsilon': 0.25,
 'C': 1.4294613383568446,
 'gamma': '0.31622776601683794',
 'degree': 0,
 'r2score_train': 0.8080529387016915,
 'mse_train': 0.1450693197797238,
 'acc_train': 0.8982526284614245,
 'r2score_val': 0.3859280363707231,
 'mse_val': 0.4635654466182942,
 'acc_val': 0.605451220314453,
 'scaler': 'StandardScaler()',
 'time': 2.7506747245788574}

Training der endgültigen Modelle mit den oben bestimmten Hyperparametern

Hier werden die oben ausgewählten Modelle erneut auf den Trainingsdatensatz trainiert, um sie anschließend auf dem Testdatensatz, welcher zu Begin zur Seite gelegt wurde, bewerten zu können.

train_df, test_df = get_datasets(logarithm=True, feature_selection=False)

X_train = train_df.drop("quality",axis=1).values
X_test = test_df.drop("quality",axis=1).values

y_train = train_df["quality"].values
y_test = test_df["quality"].values


rfr_model = RandomForestRegressor_train(X_train, y_train, hyperparameter = best_config_tree, cross_validation = False)
svr_model = SupportVectorRegressor_train(X_train, y_train, cross_validation=False, hyperparameter=best_config_svr, kernel = "rbf")

Ergebnisse

Random Forest Regression

In der folgenden Grafik werden die Regressionsmetriken der Genauigkeit und Mean Squared Error dargestellt. Da unsere Zielvariable aus diskreten Werten besteht und unsere Regressionsvorhersagen stetig sind, muss zur Berechnung der Genauigkeit der Vorhersagewert gerundet werden.

Der Random Forest Regressor erzielt gute Ergebnisse auf allen Datensätze. Die deutlich besseren Ergebnisse auf den Trainingsdatensatz lassen auf eine hohe Varianz deuten.

Das Modell erzielt nahezu die gleichen Ergebnisse auf Test- und Validierungsdatensatz.

y_pred_rfr = rfr_model.predict(X_test)

plot_metrics(y_test, y_pred_rfr, best_config_tree, modelname = "RFR")

Die folgende Tabelle zeigt die Wichtigkeit der Features auf die Vorhersage. Wie bereits erwartet, hat der Alkoholgehalt bzw der logarithmierte Alkoholgehalt den größten Einfluss auf die Vorhersage des Modells.

Im Kapitel Datenanalyse wurde bereits festgestellt dass das Feature Alkoholgehalt eine mäßig-starke Korrelation zur Zielvariablen Qualität aufweist.

plot_rfrfeatureimportance(rfr_model, train_df)

Hier wird die Vorhersage des Modells noch einmal den wahren Werten gegenübergestellt, dabei werden sowohl die gerundeten Vorhersagen als auch die stetige Vorhersage dargestellt.

plot_histograms(y_test, y_pred_rfr, modelname = "RFR")

Die Confusion Matrix gibt uns etwas mehr Aufschluss über die Qualität unseres Modells. Hier werden zusätzlich für alle Klassen der Recall und Precision berechnet.

Recall: $\frac{TP}{TP+FN}$
Precision $\frac{TP}{TP+FP}$

Man kann der Tabelle entnehmen, dass für die Qualitäten 8 und 4 eine sehr hohe Precision berechnet wurde, was daran liegt dass wenige Datenpunkte dieser Klasse sehr gut vorhergesagt werden konnten. Der hier interessantere Recall Wert gibt uns jedoch Aufschluss darüber, dass viele Weine der Qualität 4 und 8 nicht richtig klassifiziert wurden.

Ein besseres Ergebnis sieht man bei den Weinen der Qualität 5,6 und 7. Dies ist wenig überraschend, da für diese Klassen die meisten Daten zur Verfügung standen.

plot_confusion_matrix(y_test, y_pred_rfr, modelname = "RFR", datensatz = "(Testdatensatz)")

plot_correct_prediction_pairplot(y_test, y_pred_rfr, test_df)

SVR ohne Featureselection

In der folgenden Grafik werden die Regressionsmetriken der Genauigkeit und Mean Squared Error dargestellt.

Wie zuvor der Random Forest Regressor erzielt auch der Support Vector Regressor sehr gute Ergebnisse. Das Modell performt auf die Testdaten ähnlich gut wie auf den Validierungsdaten. Es ist allerdings wieder klar zu erkennen, dass die Genauigkeit, als auch der Mean Squared Error auf den Trainingsdaten deutlich besser ist.

y_pred_svr = svr_model.predict(X_test)

plot_metrics(y_test, y_pred_svr, best_config_svr, modelname="SVR")

Hier wird die Vorhersage des Models noch einmal den wahren Werten gegenübergestellt, dabei werden sowohl die gerundeten Vorhersagen als auch die stetige Vorhersage dargestellt.

plot_histograms(y_test, y_pred_svr, modelname="SVR")

Wie schon die Confusion Matrix bei dem Random Forest Regressor zeigt sich der unausgeglichene Datensatz ebenfalls in den Ergebnissen der Confusion Matrix des Support Vector Regressors.

plot_confusion_matrix(y_test, y_pred_svr, modelname="SVR", datensatz="(Testdatensatz)")

plot_correct_prediction_pairplot(y_test, y_pred_svr, test_df)

Additional Work

Ensemble Modell

Zusätzlich zu den oben beschriebenen Modellen, wurde ein Voting Ensemble der beiden Algorithmen erstellt.

Aus den anschließenden Plots ist zu erkennen, dass das erstellte Ensemble Modell nicht wesentlich verbessert. Dies könnte daran liegen, dass der Random Forest Regressor bereits intern ein Ensemblemodell aus vielen verschiedenen Entscheidungsbäumen ist und bereits bessere Ergebnisse als der Support Vector Regressor erzielt.

y_pred = np.mean(np.asarray([svr_model.predict(X_test), rfr_model.predict(X_test)]),axis = 0)

plot_metrics_ensemble(y_test, y_pred, predictions=[y_pred_svr, y_pred_rfr], modelname="SVR")

plot_histograms(y_test, y_pred, modelname="Ensemble Modell")

plot_confusion_matrix(y_test, y_pred, modelname="Ensemble Modell", datensatz="(Testdatensatz)")

Fazit

Die Ergebnisse beider Modelle sind vorzeigbar. Beide Modelle haben einen guten Mean Squared Error auf den Testdatensatz. Das Random Forest Regressor Modell hat dabei bessere Ergebnisse als der Support Vector Regressor erzielt. Die Feature Selection hat bei diesem Datensatz zu starkem Informationsverlust und somit zu deutlich schlechtere Ergnissen geführt.

Die schlechteren Ergebnisse bei den weniger vertretenen Klassen waren zu erwarten und akzeptabel, da diese auf Sicht des gesamten Datensatzes nur in sehr kleiner Anzahl vorkommen. Eine Gewichtung oder Oversampling dieser Gruppen zur Verschlechterung der Precision der häufigeren vorkommenen Klassen führen. Dies kann dazu führen, dass Modelle auf zukünftige Daten die Vorhersage der weniger vorkommenen Klassen präferenzieren und somit die Klassenverteilung der Vorhersage und die der realen Werte abweichen.

Grundsätzlich muss man mit diesem Datensatz kritisch umgehen da die subjektive Einordnung durch Weinexperten keine wissenschaftlich basierte Einordnung ist und schlechtere Ergebnisse von Modellen auch auf eine Inkonsistenz dieser Einordnung zurückzuführen werden könnte.